Real-time inland flood inundation prediction system, real-time inland flood inundation prediction device, real-time inland flood inundation prediction method, real-time inland flood inundation prediction program, computer-readable recording medium, and stored device.

Abstract

Real-time prediction of inland flood levels. [Solution] The real-time inland flood inundation prediction system 1000 includes: a water volume calculation unit 31 that calculates the current water volume in real time based on rainfall and initial water level; a predicted inundation depth distribution model storage unit 22 that stores a plurality of predicted inundation depth distribution models calculated in advance for each water volume in the prediction area; a flood arrival time calculation unit 33 that calculates the flood arrival time when the flood is expected to reach the prediction area based on rainfall intensity; a predicted inundation depth distribution model extraction unit 34 that corrects the water volume calculated in real time by the water volume calculation unit 31 to a corrected water volume considering the flood arrival time calculated by the flood arrival time calculation unit 33, and extracts a predicted inundation depth distribution model corresponding to the corrected water volume from a plurality of predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit 22; and a display unit 40 that displays the predicted inundation depth distribution model extracted by the predicted inundation depth distribution model extraction unit 34.

Description

Technical Field

[0001] The present disclosure relates to a real-time inland flooding prediction system, a real-time inland flooding prediction device, a real-time inland flooding prediction method, a real-time inland flooding prediction program, a computer-readable recording medium, and a device storing the same.

Background Art

[0002] In recent years, extremely large typhoons and frontal precipitation bands, which are thought to be caused by global warming and abnormal weather, have occurred in various places, and flooding and inundation are a concern. Therefore, flood hazard maps and the like are provided for the purpose of providing safe evacuation routes and the like. In particular, various studies have been conducted aiming at improving the accuracy of real-time prediction of water levels when inland flooding occurs.

[0003] For example, a real-time dynamic flooding simulation system that predicts flooding based on observed rainfall amounts has been proposed (Patent Document 1). However, in this real-time dynamic flooding simulation system, the prediction is only based on the rainfall amounts that have fallen in the past. For example, due to changes in rainfall amounts from the past, such as when the rainfall intensity increases or conversely decreases, there is a problem that the prediction error becomes large. On the other hand, although it is conceivable to change the flooding prediction in consideration of the prediction of rainfall amounts, in this case, the amount of calculation according to the complex terrain for each region becomes enormous, making it difficult to perform real-time flooding prediction. If real-time flooding prediction is attempted, a relatively high level of computing power is required, resulting in a problem of soaring calculation costs.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] One objective of one embodiment of this disclosure is to provide a real-time inland flood inundation prediction system, a real-time inland flood inundation prediction device, a real-time inland flood inundation prediction method, a real-time inland flood inundation prediction program, a computer-readable recording medium, and a device that stores this information, which enables highly accurate real-time prediction of inland flood water levels. Another objective of the other embodiment is to provide a real-time inland flood inundation prediction system, a real-time inland flood inundation prediction device, a real-time inland flood inundation prediction method, a real-time inland flood inundation prediction program, a computer-readable recording medium, and a device that stores this information, which further improves the accuracy of inundation depth predictions by incorporating observed water level values ​​when they become available. The description of these objectives and problems in this disclosure does not preclude the existence of other objectives and problems. Furthermore, one embodiment of this disclosure is not required to solve all of these problems. It is also possible to extract other problems from the description in the specification, drawings, and claims of this disclosure. Means for solving the problems and effects of the invention

[0006] A real-time inland flood inundation prediction system according to Embodiment 1 of the present disclosure is a real-time inland flood inundation prediction system that predicts inland flooding in a prediction area caused by rainfall in real time, under the conditions that the prediction area is divided into small watersheds, uniform rain falls in each small watershed, and a predetermined inundation depth distribution occurs for each small watershed at each location included in the prediction area, and comprises a data acquisition unit that acquires observed rainfall and predicted rainfall, a water volume calculation unit that calculates the current water volume in real time based on the observed rainfall acquired by the data acquisition unit, the predicted rainfall and the initial water level, and a predetermined calculation for each water volume in the prediction area. The system includes a predicted inundation depth distribution model storage unit for storing multiple predicted inundation depth distribution models, a flood arrival time calculation unit for calculating the expected flood arrival time in the predicted area based on rainfall intensity, a predicted inundation depth distribution model extraction unit for correcting the inundation volume calculated in real time by the inundation volume calculation unit, taking into account the flood arrival time calculated by the flood arrival time calculation unit, and extracting a predicted inundation depth distribution model corresponding to the corrected inundation volume from among the multiple predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit, and a display unit for displaying the predicted inundation depth distribution model extracted by the predicted inundation depth distribution model extraction unit. With the above configuration, by adding the flood arrival time as well as the inundation volume and converting it into an inundation depth distribution, it is possible to realize real-time inland flood inundation prediction with improved prediction accuracy.

[0007] Furthermore, the real-time inland flood inundation prediction system according to the second form further includes, in the above form, a rainfall intensity-specific water volume time change storage unit that stores the results of pre-calculated relationships between water volume and elapsed time according to rainfall intensity through flood analysis, and an inundation depth distribution storage unit for storing the relationship between flood arrival time and water volume.

[0008] Furthermore, in any of the above configurations, the real-time inland flood inundation prediction system according to Form 3 includes, at predetermined intervals, the rainfall intensity-based water volume time change storage unit, as a graph of the water level increase period, the flood analysis results representing the rainfall force when multiple different intensities of constant rainfall continue, and as a graph of the water level decrease period, the flood analysis results representing the rainfall force when multiple different intensities of constant rainfall continue. With this configuration, flood analysis when a constant rainfall intensity continues can be performed in advance.

[0009] Furthermore, in the real-time inland flood inundation prediction system according to form 4, in any of the above forms, the flood arrival time calculation unit calculates the inundation depth distribution for a time step (w+1) by averaging several neighboring points from several plots that show the relationship between the amount of inundation assumed for several different probability rainfall amounts and the flood arrival time at that time, which are recorded in the inundation depth distribution storage unit.

[0010] Furthermore, in any of the above forms, the real-time inland flood inundation prediction system according to Form 5 includes, at least, the multiple different probabilistic rainfall amounts as the probabilistic rainfall amount for a specific year and the assumed maximum scale.

[0011] Furthermore, in any of the above forms, the real-time inland flood inundation prediction system according to form 6 includes, in order to assume the amount of inundation under different probability rainfall amounts, a model of a forward-concentrated rainfall waveform where rainfall is concentrated at the beginning of the rainfall period, a mid-concentrated rainfall waveform where rainfall is concentrated in the middle of the rainfall period, and a backward-concentrated rainfall waveform where rainfall is concentrated towards the end of the rainfall period. With the above configuration, flood analysis can be performed in advance using the assumed maximum rainfall as an external force.

[0012] Furthermore, the real-time inland flood inundation prediction system according to Embodiment 7 further comprises, in any of the above embodiments, a plurality of water level sensors installed at predetermined installation locations in the prediction area, wherein the inundation volume calculation unit is connected to each of the plurality of water level sensors via a network, and the inundation volume calculation unit is configured to correct the inundation volume calculated in real time using the measured water level at the installation location observed by the water level sensor. With the above configuration, a more accurate real-time inland flood inundation prediction can be achieved by correcting the inundation volume at each installation location with the measured water level.

[0013] Furthermore, in the real-time inland flood inundation prediction system according to form 8, in any of the above forms, the inundation volume calculation unit is configured to perform real-time calculation of the inundation volume using the measured water level at the installation location observed by the water level sensor as the initial water level. With this configuration, it becomes possible to perform real-time inland flood inundation prediction using the measured water level at which inundation has already occurred as the initial water level, rather than just using the initial water level as the start of rainfall, thereby enabling more accurate inland flood prediction.

[0014] Furthermore, in any of the above embodiments, the real-time inland flood inundation prediction system according to Embodiment 9 is configured such that the water volume calculation unit complements the water level of the surrounding area with the water levels of adjacent locations among the water levels measured at multiple installation locations observed by the multiple water level sensors. With this configuration, more accurate real-time inland flood inundation prediction can be achieved by supplementing area information from point-based measured water levels.

[0015] Furthermore, another form of real-time inland flood inundation prediction system is a real-time inland flood inundation prediction system that predicts inland flooding in a prediction area caused by rainfall in real time, under the conditions that the prediction area is divided into small watersheds, uniform rain falls in each small watershed, and a pre-calculated inundation depth distribution occurs at each location included in the prediction area for each small watershed, and comprises a water volume calculation unit for calculating the current water volume in real time based on the initial water level, a predicted inundation depth distribution model storage unit for storing multiple inundation depth distribution models pre-calculated for each water volume in the prediction area, and a predicted inundation depth distribution model extraction unit for extracting a corresponding predicted inundation depth distribution model from the multiple predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit based on the water volume calculated in real time by the water volume calculation unit. With the above configuration, real-time inland flood inundation prediction is possible without using a dynamics model during real-time operation.

[0016] Furthermore, the real-time inland flood inundation prediction device according to form 10 is a real-time inland flood inundation prediction device that predicts inland flooding in a prediction area caused by rainfall in real time, under the conditions that the prediction area is divided into small watersheds, uniform rain falls in each small watershed, and a pre-calculated inundation depth distribution occurs at each location included in the prediction area for each small watershed, and includes a data acquisition unit that acquires observed rainfall and predicted rainfall, a water volume calculation unit that calculates the current water volume in real time based on the observed rainfall acquired by the data acquisition unit, the predicted rainfall and the initial water level, and stores a plurality of pre-calculated predicted inundation depth distribution models for each water volume in the prediction area. The system comprises a predicted inundation depth distribution model storage unit, a flood arrival time calculation unit for calculating the expected flood arrival time in the predicted area based on rainfall intensity, and a predicted inundation depth distribution model extraction unit for extracting a corresponding predicted inundation depth distribution model from a plurality of predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit based on the inundation volume calculated in real time by the inundation volume calculation unit. The predicted inundation depth distribution model extraction unit is configured to correct the inundation volume calculated in real time by the inundation volume calculation unit by considering the flood arrival time calculated by the flood arrival time calculation unit, and to extract a predicted inundation depth distribution model corresponding to the corrected inundation volume. With the above configuration, by adding the flood arrival time as well as the inundation volume and converting it into an inundation depth distribution, it is possible to realize real-time inland flood inundation prediction with improved prediction accuracy.

[0017] Furthermore, the real-time inland flood inundation prediction method relating to form 11 is a real-time inland flood inundation prediction method that predicts inland flooding in the prediction area caused by rainfall in real time, under the conditions that the prediction area is divided into small watersheds, uniform rain falls in each small watershed, and a predetermined inundation depth distribution occurs at each location included in the prediction area for each small watershed, and the method comprises the steps of calculating the current amount of inundation in real time based on the initial water level, and calculating the prediction area based on the amount of inundation calculated in real time The process includes a step of extracting a corresponding predicted inundation depth distribution model from a plurality of predicted inundation depth distribution models that have been pre-calculated for each amount of inundation in the area, wherein the step of extracting the corresponding predicted inundation depth distribution model includes a step of calculating the expected flood arrival time when the flood will reach the prediction area based on the rainfall intensity using a flood arrival time calculation unit, correcting the real-time calculated inundation amount to a corrected inundation amount considering the calculated flood arrival time, and extracting a predicted inundation depth distribution model corresponding to the corrected inundation amount. This makes it possible to realize real-time inland flood inundation prediction with improved prediction accuracy by adding the flood arrival time as well as the inundation amount and converting it into an inundation depth distribution.

[0018] Furthermore, the real-time inland flood inundation prediction method according to form 12 includes, in any of the above forms, a step in which the process of extracting the corresponding predicted inundation depth distribution model includes a step of preparing an inundation depth distribution storage unit that stores the relationship between the inundation volume and the time of flood arrival as inundation depth distribution data, and a rainfall intensity-specific inundation volume time change storage unit that stores rainfall intensity-specific inundation volume time change data calculated by flood analysis, which shows the relationship between the inundation volume and elapsed time according to the rainfall intensity. The process involves preparing the water level, comparing the water level (V(t)) at the current time step (w), which is the water level at the present time, with the water level time change data for each rainfall intensity stored in the water level time change storage unit, and calculating the time change amount (ΔV(R)) of the water level (V(t)) at the current time step (w) due to an arbitrary amount of rainfall (R) that occurred from the water level (V(t)) at the next time step (w+1), which is after a certain time (Δw), using the water level data for each rainfall intensity. The process involves calculating from time-varying data, estimating the water volume (V(t+1)) for the next time step (w+1) based on the calculated time-varying amount (ΔV(R)) of the water volume for the next time step (w+1), comparing the water volume (V(t+1)) for the next time step (w+1) with the inundation depth distribution data stored in the inundation depth distribution storage unit, and storing the rainfall intensity curve calculated from the rainfall waveform in the rainfall intensity curve storage unit. The process involves calculating the Kadoya-Fukushima formula in a quasi-linear storage function model calculation unit, determining the point where the rainfall intensity curve stored in the rainfall intensity curve storage unit intersects with the quasi-linear storage function model as the flood arrival time in the flood arrival time calculation unit, and comparing the flood arrival time calculated by the flood arrival time calculation unit with the inundation depth distribution data stored in the inundation depth distribution storage unit to determine the inundation volume (V) of the next time step (w+1). A (T AThe process includes: calculating the flood depth distribution; calculating the averaged flood depth distribution for the time step (w+1) by averaging a plurality of neighboring points that have been previously recorded in the flood depth distribution storage unit; and repeatedly performing the steps from comparing the time-varying data of inundation volume by rainfall intensity to obtaining the averaged flood depth distribution for the time step (w+2) after a certain time period (Δw) has elapsed since the time step (w+1), thereby obtaining the averaged flood depth distribution for each time. In this way, in the real-time operation phase, the flood depth distribution is retrieved according to the inundation volume and flood arrival time calculated from an arbitrary rainfall waveform, thereby reducing the computational load in the real-time operation phase and enabling real-time inland flood prediction.

[0019] Furthermore, the real-time inland flood inundation prediction method according to form 13 includes, in any of the above forms, a step in preparing the inundation depth distribution storage unit, a step of creating a rainfall intensity formula for each of several different probabilistic rainfall amounts, and a step of performing a flood analysis on the rainfall intensity formula for each created probabilistic rainfall amount, using a forward-concentrated rainfall waveform where rainfall is concentrated at the beginning of the rainfall period, an intermediate-concentrated rainfall waveform where rainfall is concentrated in the middle of the rainfall period, and a backward-concentrated rainfall waveform where rainfall is concentrated towards the end of the rainfall period as external forces, and outputting the flood analysis results at predetermined intervals. This makes it possible to perform a flood analysis using the assumed maximum rainfall as an external force in advance.

[0020] Furthermore, in any of the above forms, the real-time inland flood inundation prediction method relating to form 14 includes, at least, the multiple different probabilistic rainfall amounts for a specific year and the assumed maximum scale.

[0021] Furthermore, the real-time inland flood inundation prediction method according to form 15 includes, in any of the above forms, a step in preparing the inundation depth distribution storage unit, a step of performing a flood analysis as a graph of the water level increase period, using rainfall forces when multiple constant rainfall intensities of different intensities continue, and outputting the flood analysis results at predetermined intervals, and a step of performing a flood analysis as a graph of the water level decrease period, using rainfall forces when multiple constant rainfall intensities of different intensities continue, and outputting the flood analysis results at predetermined intervals. This makes it possible to perform a flood analysis in advance when a constant rainfall intensity continues.

[0022] Furthermore, the real-time inland flood inundation prediction method according to form 16 further includes, in any of the above forms, a step of acquiring the water level for each installation location observed by a plurality of water level sensors installed at predetermined locations in the prediction area, and a step of correcting the real-time calculated inundation volume using the water levels at the installation locations observed by the water level sensors. By correcting the inundation volume at each installation location with the measured water level, a more accurate real-time inland flood inundation prediction can be achieved.

[0023] Furthermore, the real-time inland flood inundation prediction method according to Embodiment 17 includes, in any of the above embodiments, a step of correcting the amount of inundation, which involves using the measured water level at the installation location observed by the water level sensor as the initial water level and performing a real-time calculation of the amount of inundation. This makes it possible to perform real-time inland flood inundation prediction using the measured water level at which inundation has already occurred as the initial water level, rather than being limited to real-time inland flood inundation prediction using the time when rainfall begins as the initial water level, thereby enabling more accurate inland flood prediction.

[0024] Furthermore, in any of the above embodiments, the real-time inland flood inundation prediction method according to Embodiment 18 includes a step of correcting the amount of inundation, in which the water level of the surrounding area is supplemented by the water level of adjacent locations among the water levels measured at multiple installation locations observed by the multiple water level sensors. By supplementing the surrounding water level from the measured water level, a more accurate real-time inland flood inundation prediction can be achieved.

[0025] Furthermore, the real-time inland flood inundation prediction program according to form 20 is a real-time inland flood inundation prediction program for predicting inland flooding in a predicted area caused by rainfall, under the conditions that the predicted area is divided into small watersheds, uniform rain falls within each small watershed, and a pre-calculated inundation depth distribution occurs for each small watershed at each location included in the predicted area. The program enables the computer to perform the following functions: a function to calculate the current inundation volume in real time based on the initial water level; a function to extract a corresponding predicted inundation depth distribution model from a plurality of pre-calculated predicted inundation depth distribution models for each inundation volume in the predicted area based on the inundation volume calculated in real time; and a function to calculate the expected flood arrival time when the flood is expected to reach the predicted area based on the rainfall intensity, correct the inundation volume calculated in real time to a corrected inundation volume considering the calculated flood arrival time, and extract a predicted inundation depth distribution model corresponding to the corrected inundation volume. This allows for real-time inland flood inundation prediction with improved accuracy by adding flood arrival time to the water volume and converting it into an inundation depth distribution.

[0026] Furthermore, the computer-readable recording medium or storage device relating to Form 20 stores the program relating to the above form. The recording medium includes magnetic disks such as CD-ROM, CD-R, CD-RW, flexible disk, magnetic tape, MO, DVD-ROM, DVD-RAM, DVD-R, DVD+R, DVD-RW, DVD+RW, Blu-ray®, HD DVD (AOD), optical disks, magneto-optical disks, semiconductor memory, and other media capable of storing programs. In addition to programs stored and distributed on the above recording medium, programs also include those distributed by download via network lines such as the Internet. Furthermore, the storage device includes general-purpose or dedicated devices on which the above program is implemented in a state in which it can be executed in the form of software or firmware. Furthermore, each process and function included in the program may be executed by program software that can be executed on a computer, or each part of the processing may be implemented in a form in which program software and partial hardware modules that realize some elements of hardware are mixed. [Brief explanation of the drawing]

[0027] [Figure 1] This block shows a real-time inland flood inundation prediction system according to Embodiment 1. [Figure 2] This block represents a networked, real-time inland flood inundation prediction system. [Figure 3] This is a schematic diagram showing a real-time inland flood inundation prediction method according to Embodiment 1. [Figure 4] This flowchart shows the real-time inland flood inundation prediction method according to Embodiment 1. [Figure 5] This graph shows a flood analysis assuming a sustained rainfall intensity. [Figure 6] This graph shows the time-dependent changes in water inundation volume according to rainfall intensity. [Figure 7]Figures 7A to 7D are graphs illustrating exception handling to account for delay time. [Figure 8] This is a graph of the water level increase period, used in the basic process of determining the amount of water inundated at each time step. [Figure 9] This is a graph of the water level decrease period, used in the basic process of determining the water volume at each time step. [Figure 10] This graph shows the change in water volume over time. [Figure 11] This graph shows a comparison of maximum flood depths. [Figure 12] This graph shows the results of a comparison of maximum flow fluxes. [Figure 13] This graph shows a comparison of flood depths every hour on the hour. [Figure 14] This is an illustrative diagram showing the flood depth distribution at 11:00 PM on the 20th, using the method of Comparative Example 1. [Figure 15] This is an illustrative diagram showing the flood depth distribution at 23:00 on the 20th, using the method described in Example 1. [Figure 16] This is a schematic diagram showing a real-time inland flood inundation prediction method according to Example 2. [Figure 17] This flowchart shows the procedure for the real-time operation phase according to Example 2. [Figure 18] This is a conceptual diagram showing how the flood depth is calculated in the time step according to Example 2. [Figure 19] This graph shows the change in water volume over time. [Figure 20] This graph compares the maximum flood depths. [Figure 21] This graph compares the maximum flow flux. [Figure 22] This graph compares the flood depth at one-hour intervals. [Figure 23] This graph shows the accuracy rate of color schemes every hour on the hour. [Figure 24] This is an illustrative diagram showing the maximum flood depth distribution using the method of Comparative Example 2. [Figure 25] This is an illustrative diagram showing the maximum flood depth distribution using the method of Example 2. [Figure 26] This is an illustrative diagram showing the difference in the maximum flood depth distribution between Comparative Example 2 and Example 2. [Figure 27] This graph shows the accuracy rate of predicting floor-level flooding every hour on the hour. [Figure 28] This is an illustrative diagram showing the flood depth distribution using the method of Comparative Example 2 at 5:00 PM on the 19th. [Figure 29] This is an illustrative diagram showing the flood depth distribution using the method of Example 2 at 17:00 on the 19th. [Modes for carrying out the invention]

[0028] The embodiments of this disclosure will be described below with reference to the drawings. However, the embodiments shown below are examples for concretizing the technical concept of this disclosure, and this disclosure is not limited to the following. Furthermore, this specification does not limit the members shown in the claims to the members of the embodiments. In particular, the dimensions, materials, shapes, relative arrangements, etc. of the components described in the embodiments are not intended to limit the scope of this disclosure to those, unless otherwise specifically stated, but are merely illustrative examples. Note that the size and positional relationships of the members shown in each drawing may be exaggerated for clarity of explanation. Furthermore, in the following description, the same name and reference numeral indicate the same or similar members, and detailed explanations are omitted as appropriate. Furthermore, each element constituting this disclosure may be configured such that multiple elements are made of the same material, with one material serving multiple elements, or conversely, the function of one material may be shared among multiple materials.

[0029] The real-time inland flood inundation prediction device used in the embodiments of this disclosure and the computer, printer, external storage device, and other peripheral devices connected thereto for operation, control, display, and other processing are connected electrically, magnetically, or optically via serial connections such as IEEE1394, RS-232x, RS-422, RS-423, RS-485, USB, parallel connections, or networks such as 10BASE-T, 100BASE-TX, and 1000BASE-T. The connection is not limited to a wired physical connection, but may also be a wireless connection using wireless LAN such as IEEE802.1x, Bluetooth®, or other radio waves, infrared, optical communication such as NFC. Furthermore, memory cards, magnetic disks, optical disks, magneto-optical disks, semiconductor memory, etc. can be used as recording media for data exchange and setting storage. In this specification, the term "real-time inland flood inundation prediction device" includes not only the real-time inland flood inundation prediction device itself, but also an inland flood inundation prediction system that combines it with peripheral devices such as a computer and external storage device. [Embodiment 1]

[0030] The real-time inland flood inundation prediction system predicts inland flooding in a prediction area that is subject to inundation prediction. Figure 1 shows the real-time inland flood inundation prediction system 1000 according to Embodiment 1 of this disclosure. The real-time inland flood inundation prediction system 1000 shown in this figure predicts an inundation depth distribution map of a prediction area based on rainfall data within that prediction area and displays it on the display unit 40. Here, the prediction area refers to the entire area in which the real-time inland flood inundation prediction system 1000 predicts the presence or absence of flooding and the degree of flooding risk. The range of this prediction area is not particularly specified. The range of the prediction area can be arbitrarily determined by the system user within the target area, for example, it can be set to a specific municipality. The inundation depth distribution map is displayed on a map, showing the locations where flooding has occurred and the water depth. Furthermore, the real-time inland flood inundation prediction system divides the prediction area into smaller watersheds, assumes uniform rainfall within each watershed, and predicts inland flooding in the prediction area in real time under the conditions that a pre-calculated inundation depth distribution occurs at each location within the prediction area for each watershed.

[0031] The real-time inland flood inundation prediction system 1000 includes a real-time inland flood inundation prediction device 100. The real-time inland flood inundation prediction device 100 can be configured with dedicated hardware, or it can be configured by installing an inland flood inundation prediction program on a general-purpose or dedicated computer. This real-time inland flood inundation prediction system 1000 includes a data acquisition unit 10, a data storage unit 20, a calculation unit 30, a display unit 40, and an operation unit 50. (Data acquisition unit 10)

[0032] The data acquisition unit 10 is a component for acquiring data from an external source. In this case, it functions as a component for acquiring rainfall data. Rainfall data is typically the amount of rainfall per unit time. The rainfall is the observed rainfall (past rainfall) observed in the past. The rainfall data may also include predicted rainfall (future rainfall) which is a prediction of future rainfall.

[0033] These rainfall data are acquired via a network. This makes it easy to update the information with the latest data in real time. For this reason, the data acquisition unit 10 is equipped with a communication function to connect to a general-purpose network line such as the Internet, or to a specific network via a dedicated line.

[0034] The rainfall data includes analyzed rainfall and short-term precipitation forecasts for the predicted area. Analyzed rainfall is the amount of rain that actually fell within a predetermined time period from the current time. Short-term precipitation forecasts are the amount of rain that is expected to fall within a predetermined time period from the current time. In this embodiment, this predetermined time period is set to every hour. However, any time unit, such as every 30 minutes, may be used.

[0035] For example, the rainfall data distributed by the Japan Meteorological Business Support Center includes analyzed rainfall and short-term precipitation forecasts, and predicted rainfall up to 6 hours in advance for areas divided into 1km meshes of topographic data is updated and distributed every 30 minutes. Therefore, the data acquisition unit 10 acquires such data sequentially and sends it to the calculation unit 30. The rainfall data acquired by the data acquisition unit 10 can also be stored in the data storage unit 20. For example, the data storage unit 20 may be provided with a rainfall data storage unit to hold the rainfall data.

[0036] There is no specific source for collecting rainfall data; for example, rainfall data distributed by the Japan Meteorological Agency or the Japan Meteorological Business Support Center can be used, or data can be collected directly by installing your own rainfall observation equipment. Furthermore, the specified time is not limited to one hour; it may be a shorter time (e.g., 30 minutes) or a longer time (e.g., 2 hours). (Data storage unit 20)

[0037] The data storage unit 20 is a component for storing various types of data. This data storage unit 20 includes a flood depth distribution storage unit 21, a predicted flood depth distribution model storage unit 22, a rainfall intensity-specific inundation volume time change storage unit 23, a rainfall intensity curve storage unit 26, and a quasi-linear storage function model storage unit 27. (Flood depth distribution preservation section 21)

[0038] The inundation depth distribution storage unit 21 stores a group of inundation depth distributions. The inundation depth distribution shows the relationship between the time of flood arrival and the amount of water inundated. This inundation depth distribution can be represented as an inundation depth distribution map showing the inundation situation of a predicted area at a specific time. This group of inundation depth distributions is generated in advance by the calculation unit 50 or an external computer, etc., and stored in the inundation depth distribution storage unit 21.

[0039] The inundation depth distribution storage section 21 can include multiple rainfall waveform models to account for inundation volumes under different probability rainfall amounts. Specifically, it includes models for a forward-concentrated rainfall waveform where rainfall is concentrated at the beginning of the rainfall period, a mid-concentrated rainfall waveform where rainfall is concentrated in the middle of the rainfall period, and a backward-concentrated rainfall waveform where rainfall is concentrated towards the end of the rainfall period. This allows for pre-inundation flood analysis using the assumed maximum rainfall as the external force.

[0040] The predicted inundation depth distribution model storage unit 22 is a component for storing multiple predicted inundation depth distribution models that have been pre-calculated for each amount of flooding in the predicted area. (Storage section 23 of time-dependent changes in water volume by rainfall intensity)

[0041] The Rainfall Intensity-Specific Water Volume Time Change Storage Unit 23 is a component for storing the results of a pre-calculated relationship between water volume and elapsed time according to rainfall intensity, based on flood analysis. Here, the Rainfall Intensity-Specific Water Volume Time Change Storage Unit 23 stores Rainfall Intensity-Specific Water Volume Time Change Data. Rainfall Intensity-Specific Water Volume Time Change Data is, for example, a graph with elapsed time on the horizontal axis and water volume on the vertical axis. Furthermore, as shown in Figure 16 described later, Rainfall Intensity-Specific Water Volume Time Change Data can be created for both the water level rise period, when the rainfall force is greater than the drainage capacity in the predicted area, and the water level fall period, when the rainfall force is less than the drainage capacity in the predicted area, and stored in the Rainfall Intensity-Specific Water Volume Time Change Storage Unit 23. In other words, the rainfall intensity-based water volume time change storage unit 23 can include, at predetermined time intervals, the flood analysis results for the period of water level increase, which represent the rainfall force when a constant rainfall intensity of multiple different intensities continues, and the flood analysis results for the period of water level decrease, which represent the rainfall force when a constant rainfall intensity of multiple different intensities continues. This makes it possible to perform a flood analysis in advance for the case of a continuous constant rainfall intensity.

[0042] The rainfall intensity curve storage unit 26 is a component for storing the rainfall intensity curve calculated from the rainfall waveform.

[0043] The quasi-linear storage function model storage unit 27 is a component for storing the quasi-linear storage function model. The quasi-linear storage function model is one of the runoff calculations used to determine flood arrival times in watersheds where land use categories such as fields, building sites, and forests are mixed. In the quasi-linear storage function model, the intersection of the rainfall intensity curve and the Kadoya-Fukushima formula curve is calculated as the flood arrival time. (Computation unit 30)

[0044] The calculation unit 30 is a component for performing various calculations, and in this case it performs the functions of a water volume calculation unit 31, a flood arrival time calculation unit 33, a predicted inundation depth distribution model extraction unit 34, and a quasi-linear storage type function model calculation unit 35.

[0045] The water level calculation unit 31 is a component that calculates the current water level in real time based on the observed rainfall acquired by the data acquisition unit 10, the predicted rainfall, and the initial water level.

[0046] The flood arrival time calculation unit 33 is a component for calculating the expected flood arrival time in the predicted area, based on rainfall intensity. Here, the point where the rainfall intensity curve stored in the rainfall intensity curve storage unit 26 intersects with the quasi-linear storage function model stored in the quasi-linear storage function model storage unit 27 is calculated as the flood arrival time.

[0047] The predicted inundation depth distribution model extraction unit 34 corrects the inundation volume calculated in real time by the inundation volume calculation unit 31 to a corrected inundation volume considering the flood arrival time calculated by the flood arrival time calculation unit 33, and extracts a predicted inundation depth distribution model corresponding to the corrected inundation volume from among multiple predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit 22.

[0048] The quasi-linear storage function model calculation unit 35 is a component for calculating the quasi-linear storage function model, specifically the Kadoya-Fukushima formula.

[0049] This configuration allows for real-time inland flood inundation prediction with improved accuracy by adding flood arrival time to the water volume and converting it into an inundation depth distribution. (Flood arrival time calculation unit 33)

[0050] Furthermore, the flood arrival time calculation unit 33 can calculate the inundation depth distribution for a time step (w+1) by averaging several neighboring points from among several plots recorded in the inundation depth distribution storage unit 21, which show the relationship between the inundation volume expected for multiple different probabilistic rainfall amounts and the flood arrival time at that time. Here, the multiple different probabilistic rainfall amounts can include at least one of the probabilistic rainfall amounts for a specific year and the assumed maximum scale. Examples of probabilistic rainfall amounts for a specific year include 2-year probability, 10-year probability, 50-year probability, 100-year probability, 500-year probability, etc.

[0051] The calculation unit 30 also includes a database access unit 32 for accessing the data storage unit 20. The database access unit 32 is an interface for accessing the data storage unit 20, such as the inundation depth distribution storage unit 21. In addition to a configuration in which the database access unit 32 accesses a storage medium such as a hard disk or semiconductor memory included in the real-time inland flood inundation prediction device 100, it may also be configured to access a database connected to the outside of the real-time inland flood inundation prediction device 100. For example, the real-time inland flood inundation prediction device 100 or the real-time inland flood inundation prediction system 1000 can also be constructed by using external storage such as a NAS or file server connected via a network line as the data storage unit 20.

[0052] Furthermore, the calculation unit 30 can also perform the functions of an inundation depth distribution group creation unit 33, which creates an inundation depth distribution group, and a pre-calculated graph creation unit 34, which creates a pre-calculated graph, as shown in the real-time inland flood inundation prediction system 1000' in Figure 2. However, since the inundation depth distribution group and pre-calculated graph can be created in advance and stored in the data recording unit 20, it is not necessarily required to create them in the real-time inland flood inundation prediction device 100. For example, they can be created using an external computer or server and registered in the data storage unit 20 or other databases, respectively. In this case, it is not necessary to provide the inundation depth distribution group creation unit 33 or the pre-calculated graph creation unit 34 in the real-time inland flood inundation prediction device 100. Figure 1 shows an example of such a configuration.

[0053] Such a calculation unit 30 can use, for example, a CPU. However, it is not limited to a CPU; a Graphics Processing Unit (GPU), such as a GPGPU, may also be used. By using such a GPGPU (General-Purpose computing on Graphics Processing Units), the CPU load is reduced, the calculation speed is improved, and the prediction results are made more accurate, while the system user can easily check the prediction results using the display unit 40 described later. For example, CUDA (product name) can be used as the GPGPU. (Display section 40)

[0054] The display unit 40 is a component for displaying the predicted flood depth distribution model extracted by the predicted flood depth distribution model extraction unit 34. The display unit 40 can display future flood area predictions and past flood depth distributions. Such a display unit 40 can utilize displays such as liquid crystal displays, organic EL displays, or CRT displays. Alternatively, the display unit and input unit may be combined into a touch panel.

[0055] The display unit 40 has a map display area for displaying a map of the prediction area. In the map display area, the map is displayed at a predetermined magnification. The map magnification can be increased or decreased by operating the zoom buttons provided in the corners of the map display area. Alternatively, it may be increased or decreased by operating the scroll button of the mouse that constitutes the operation unit 50. Also, for data management purposes, grid lines are displayed vertically and horizontally with a predetermined width, and the area is displayed in blocks demarcated by these lines. Hereinafter, the area demarcated by the lines will be referred to as a block (reference area mesh). The map display magnification can also be changed as appropriate. (Operation unit 50)

[0056] The operation unit 50 is a component for performing various operations and consists of, for example, a keyboard, a pointing device such as a mouse, a console, etc. Furthermore, by using a touch panel, the operation unit 50 and the display unit 40 can be made common. (Server / Client)

[0057] Furthermore, by making the calculation unit a server device and the display unit 40 the display screen of a client device connected to this server device, users can access the server device from the outside using the client device and view the prediction results obtained by the calculation unit. An example of this is shown in Figure 2. In this case, the operation unit 50 can operate the client device CL connected to the server device SV via the network, for example, by using a touch panel or mouse.

[0058] The display unit 40 displays the user interface screen of the inland flood inundation prediction program. As described above, the user interface screen of the inland flood inundation prediction program includes not only the display unit 40 connected to a standalone computer, but also the display screen of a client device that accesses a server device on which the inland flood inundation prediction program is installed, as shown in Figure 2. By making the rendering web-based, versatility can be ensured. Client devices that can be used include smartphones, mobile phones, personal computers, etc., that have communication capabilities to access the server device.

[0059] This configuration allows for the display of inundation depth maps with minimal overhead, rather than calculating and creating future inundation depth maps each time. Instead, it selects and displays maps from a pre-calculated set of inundation depth maps according to their corresponding relationships. This enables real-time inland flood prediction without increasing computational performance. (Water level sensor WS)

[0060] The real-time inland flood inundation prediction system 1000 may also include multiple water level sensors WS. Each water level sensor WS is installed at a predetermined location in the prediction area to detect the presence or absence of flooding. The water level sensors WS may also be equipped with the function of a water level gauge to measure water levels.

[0061] Each water level sensor WS is connected to a real-time inland flood inundation prediction device via a network. In this case, the data acquisition unit 10 functions as an input interface to acquire information such as the presence or absence of flooding and water levels acquired by the water level sensors WS, in addition to rainfall data. This allows for more accurate real-time inland flood inundation prediction by correcting the amount of water inundated at each installation location with the measured water level.

[0062] The water volume calculation unit 31 of the real-time inland flood inundation prediction device can correct the water volume calculated in real time using the measured water level at the installation location observed by the water level sensor WS. By correcting the water volume at each installation location with the measured water level, more accurate real-time inland flood inundation prediction can be achieved.

[0063] Furthermore, the water volume calculation unit 31 may be configured to perform real-time calculations of the water volume using the measured water level at the installation location observed by the water level sensor WS as the initial water level. With such a configuration, it becomes possible to perform real-time inland flood predictions not only by using the initial water level as the start of rainfall, but also by using the measured water level at which flooding has already occurred as the initial water level, thereby enabling more accurate inland flood predictions.

[0064] Furthermore, the water level calculation unit 31 may be configured to complement the water level of the surrounding area using adjacent measured water levels from among the measured water levels at multiple installation locations observed by multiple water level sensors WS. This allows for more accurate real-time inland flood inundation prediction by complementing area information from the measured water level points. In particular, the accuracy of the inundation depth distribution can be improved by calculating and complementing area data from point data such as measured water levels at multiple installation locations. As an interpolation method to obtain area data from such point data, known methods such as TIN interpolation (Triangulated Irregular Network), kriging method, spline method, inverse distance weighting method, natural nearest neighbor interpolation method, modified Shepard method, Radial Basis Function, average method, TOPOGRID, etc., can be used as appropriate.

[0065] Thus, if water level gauges are installed within the prediction area for which the real-time inland flood inundation prediction system 1000 performs inundation predictions, the accuracy of the real-time inland flood inundation prediction can be improved by utilizing the data from these water level gauges. Figures 1 and 2 show examples where multiple water level sensors WS are installed within the prediction area.

[0066] Here, the water level sensor WS, also known as a flood sensor, is a device that observes water levels. In addition to being a device that measures the water level numerically, the water level sensor WS may also be a device that detects when the water level reaches a predetermined level. For example, a flood sensor can be used as a water level sensor WS if the predetermined level is set to the water level at which flooding is determined, and the sensor is configured to activate when the water level reaches this predetermined level.

[0067] These water level sensors (WS) are installed at multiple locations within the predicted flood area. Examples include main roads in urban areas, designated evacuation sites, public facilities such as city halls, ponds, and rivers. The locations where the water level sensors (WS) are installed are called water level observation points.

[0068] The water level sensor WS outputs a water level signal to the flood prediction device. The water level sensor WS is connected to the flood prediction device by wire or wireless connection. Each water level sensor WS can be connected directly to the flood prediction device as shown in Figure 1, or it can be connected via a repeater. In the example in Figure 2, multiple water level sensor WS are wirelessly connected to the flood prediction device via a repeater RT. For example, multiple water level sensor WS and the repeater RT are connected using a standardized communication method such as WiFi, Bluetooth (BLE), or ZigBee (all are product or service names), and the repeater RT and the flood prediction device are connected via public lines such as telephone lines or fiber optics. [Real-time inland flood inundation prediction method]

[0069] Here, we will explain a real-time inland flood inundation prediction method that predicts inland flooding in a predicted area caused by rainfall, under the conditions that the predicted area is divided into small watersheds, uniform rainfall occurs within each small watershed, and a pre-calculated inundation depth distribution occurs at each location within the predicted area. First, the water volume calculation unit 31 calculates the current water volume in real time based on the initial water level. Next, based on the water volume calculated in real time by the water volume calculation unit 31, the predicted inundation depth distribution model extraction unit 34 extracts the corresponding predicted inundation depth distribution model from a plurality of predicted inundation depth distribution models pre-calculated for each water volume in the predicted area. In the process of extracting the corresponding predicted inundation depth distribution model, the flood arrival time calculation unit 33 calculates the expected flood arrival time in the predicted area based on rainfall intensity, corrects the real-time calculated inundation volume to a corrected inundation volume considering the calculated flood arrival time, and extracts the predicted inundation depth distribution model corresponding to the corrected inundation volume using the predicted inundation depth distribution model extraction unit 34. By adding the flood arrival time as well as the inundation volume and converting it into an inundation depth distribution, it is possible to realize real-time inland flood inundation prediction with improved prediction accuracy.

[0070] Furthermore, in the process of the predicted inundation depth distribution model extraction unit 34 extracting the corresponding predicted inundation depth distribution model, the process includes preparing an inundation depth distribution storage unit 21 that stores the relationship between the inundation volume and the time of flood arrival as inundation depth distribution data, and preparing a rainfall intensity-specific inundation volume time change storage unit 23 that stores rainfall intensity-specific inundation volume time change data calculated by flood analysis, which shows the relationship between the inundation volume and elapsed time according to the rainfall intensity, and the inundation volume at the current time (known The process involves comparing the amount of water inundated (V(t)) at the current time step (w), which is defined as the amount of water inundated (V(t)), with the time-varying water inundation data for different rainfall intensities stored in the rainfall intensity-specific water inundation time-varying data storage unit 23, and calculating the amount of change in water inundated (ΔV(R)) due to an arbitrary amount of rainfall (R) that occurred from the amount of water inundated (V(t)) at the current time step (w) to the next time step (w+1), which is after a certain time (Δw), from the rainfall intensity-specific water inundation time-varying data. The process involves: estimating the water volume (V(t+1)) for the next time step (w+1) based on the time change (ΔV(R)) of the water volume for the next time step (w+1); comparing the water volume (V(t+1)) for the next time step (w+1) with the inundation depth distribution data stored in the inundation depth distribution storage unit 21; storing the rainfall intensity curve calculated from the rainfall waveform in the rainfall intensity curve storage unit 26; and applying the Kadoya-Fukushima formula to a quasi-linear storage function. The process involves the model calculation unit 35 performing calculations, the point where the rainfall intensity curve stored in the rainfall intensity curve storage unit 26 and the quasi-linear storage function model stored in the quasi-linear storage function model storage unit 27 intersect, and the flood arrival time calculation unit 33 calculates this point as the flood arrival time, and then the flood arrival time calculated by the flood arrival time calculation unit 33 is compared with the inundation depth distribution data stored in the inundation depth distribution storage unit 21 to determine the inundation volume (V) for the next time step (w+1). A (T AThe process may include the steps of: calculating the inundation depth distribution; calculating the averaged inundation depth distribution for the next time step (w+1) by averaging multiple neighboring points that have been recorded in advance in the inundation depth distribution storage unit 21; and repeatedly obtaining the averaged inundation depth distribution for the next time step (w+2) after a certain time period (Δw) has elapsed from the next time step (w+1) by comparing it with the time-varying data of the inundation volume by rainfall intensity, thereby obtaining the averaged inundation depth distribution for each time. In this way, in the real-time operation phase, the inundation depth distribution is retrieved according to the inundation volume and flood arrival time calculated from the arbitrary rainfall waveform, thereby reducing the computational load in the real-time operation phase and enabling real-time inland flood prediction. [Comparative Example]

[0071] To realize a real-time inland flood prediction system capable of determining the presence or absence of flooding above floor level in real time, conventional methods have relied on dynamic calculations using two-dimensional planar unsteady flow calculations. However, this method is computationally intensive, making it difficult to predict flooding over a wide area.

[0072] Therefore, in this embodiment, a statistical model is employed in which the inundation depth distribution calculated in advance using a dynamic model is saved during the preparation phase, and these inundation depth distributions are retrieved in accordance with an arbitrary rainfall waveform during the real-time operation phase. This reduces the computational load and shortens the computation time during the real-time operation phase, enabling the evaluation of flooding above floor level. It should be noted that all flood analysis models used in this embodiment do not target external flooding accompanied by levee breaches, but rather flooding caused by overflow, flooding, and inundation due to poor drainage (referred to as "internal flooding" in this specification). [Real-time inland flood inundation prediction system]

[0073] Figure 3 shows an overview of the real-time inland flood inundation prediction method according to Embodiment 1. The real-time inland flood inundation prediction system 1000 includes an inundation depth distribution storage unit 21, a rainfall intensity-specific water volume time change storage unit 23, and a calculation unit 30. [Real-time inland flood inundation prediction method]

[0074] The following describes each step of the real-time inland flood inundation prediction method according to Embodiment 1, with reference to the flowchart in Figure 4. The real-time inland flood inundation prediction method can be broadly divided into a preparation phase and a real-time operation phase. The preparation phase will be described here. (Preparation Phase)

[0075] In the preparation phase, the inundation depth distribution storage unit 21 and the rainfall intensity-specific water volume time change storage unit 23 are prepared. This corresponds to the process of preparing the inundation depth distribution data and the rainfall intensity-specific water volume time change data in step S401 of the flowchart in Figure 4. The inundation depth distribution storage unit 21 is a component for storing the results of a flood analysis performed with the assumed maximum rainfall as the external force. The rainfall intensity-specific water volume time change storage unit 23 is a component for storing the results of a flood analysis performed when a constant rainfall intensity is maintained. Alternatively, the results of a flood analysis performed with the assumed maximum rainfall as the external force may be stored in the rainfall intensity-specific water volume time change storage unit 23. Conventional flood analysis models using two-dimensional plane unsteady flow calculations can be used for these flood analyses with the assumed maximum rainfall as the external force and for flood analyses when a constant rainfall intensity is maintained.

[0076] These inundation depth distribution storage units 21 and rainfall intensity-specific inundation volume time change storage units 23 can utilize storage media such as fixed disks or semiconductor memory. Furthermore, it goes without saying that separate media can be prepared for the inundation depth distribution storage unit 21 and the rainfall intensity-specific inundation volume time change storage unit 23, or the same media can be used to store data in separate areas. (Flood analysis using the assumed maximum rainfall as an external force)

[0077] This section explains flood analysis using the assumed maximum rainfall as the external force. Following existing guidelines, a rainfall waveform of the assumed maximum scale is created, and this is used as the external force for the flood analysis. The input conditions and output values ​​(time changes in inundation depth and water volume) for this flood analysis are shown in Equations 1 and 2. In this flood analysis, the initial condition is assumed to be a state with no inundation depth, and the calculation is performed up to the time when the maximum water volume occurs.

[0078] [Number 1] TIFF2026059098000002.tif10147[Number 2] TIFF2026059098000003.tif11147 In the above formula, i, j: Lattice numbers in the x and y directions of the flood analysis [-]; dx, dy: Lattice sizes in the x and y directions of the flood analysis [m]; T A,int : Each time discretized for the time interval of the rainfall waveform of the assumed maximum rainfall and the result output time interval of the flood analysis with the rainfall waveform of the assumed maximum rainfall as the external force [h]; hs0(i, j): Submergence depth distribution in the situation where there is no water depth (indicating that all water depths at lattice number (i, j) are 0.0 m.); Rmax(T): Assumed maximum rainfall at time T [mm / h]; hs A,int (i, j, T int ): Submergence depth of the flood analysis result with the assumed maximum rainfall as the external force at lattice number (i, j) at the result output time Tint of the flood analysis [m]; f{hs(i, j), R(T)}: A function that returns the submergence depth at lattice number (i, j) at time T as the flood analysis result when the initial submergence depth at lattice number (i, j) is hs(i, j) and R(T) is given as the rainfall external force at time T [m]; V A,int (T int ): The water volume (m 3 ) at the result output time Tint of the flood analysis with the assumed maximum rainfall as the external force. (Submergence depth distribution storage unit 21)

[0079] Using the submergence depth distribution output for each T A,int by the flood analysis with the assumed maximum rainfall as the external force described above, linearly interpolating these submergence depths with respect to the time direction, for any time T A,int existing between the output time T A,int+1 and the next time T A , the submergence depth distribution hs A (i, j, T A ) and the water volume V A (T AThe relationship between the amount of water inundated and the inundation depth distribution is then calculated and stored in the inundation depth distribution storage unit 21 so that it can be used in the real-time operation phase. By performing these time-consuming mechanical calculations in advance and storing the results in the inundation depth distribution storage unit 21, the necessary inundation depth distribution data can be retrieved from the stored results in the real-time operation phase, enabling processing in a short time with a light load and realizing real-time inland flood inundation prediction. (Flood analysis under the assumption of sustained rainfall intensity)

[0080] Furthermore, in order to understand the distribution of inundation depth after a given amount of rainfall, a flood analysis is performed assuming a constant rainfall intensity. This is shown in Figure 5. The input conditions and output values ​​(time changes in inundation depth and water volume) for this flood analysis are shown in Equations 3, 4, and 5.

[0081] This flood analysis involves performing multiple flood analyses of two types: one for periods of rising water levels (Equations 3 and 5) and another for periods of falling water levels (Equations 4 and 5). In the flood analysis for periods of rising water levels, the initial condition is assumed to be a state of no inundation depth, and rainfall forces are applied assuming multiple constant rainfall intensities are maintained. The results of the flood analysis for periods of rising water levels assume a situation where the inundation volume increases over time from a state of no inundation volume until the inundation volume becomes steady. On the other hand, in the flood analysis for periods of falling water levels, the initial condition is assumed to be the inundation depth distribution when the maximum inundation volume at the assumed maximum occurs, and rainfall forces are applied assuming multiple constant rainfall intensities are maintained. The flood analysis for periods of falling water levels assumes that the drainage volume of the target basin is greater than the rainfall amount applied as rainfall forces. Therefore, the results of the flood analysis for periods of falling water levels assume a situation where the inundation volume decreases over time from the maximum inundation volume at the assumed maximum, until the inundation volume becomes steady.

[0082] [Math 3] TIFF2026059098000004.tif10147[Math 4] TIFF2026059098000005.tif19147[Number 5] In the above formula in TIFF2026059098000006.tif19147, T B: Elapsed time [h] in flood analysis when a constant rainfall intensity is maintained; R const (T): Rainfall intensity [mm / h] of a rainfall waveform where a constant rainfall intensity continues at time T (This value is constant regardless of time T because the rainfall intensity remains constant). T p,A : The time [h] at which the maximum inundation occurred in a flood analysis using the assumed maximum rainfall as the external force; hs B,up (i,j,T,R): Assuming no initial water level is the initial condition, the flood depth [m] at grid number (i,j) at time T when a constant rainfall intensity R [mm / h] continues for T hours; V B,up (T,R): Flood volume (m 3 ); hs B,down (i,j,T,R): The inundation depth [m] at grid number (i,j) at time T, assuming a constant rainfall intensity R [mm / h] continues for T hours, with the initial condition being the inundation depth distribution when the maximum flood volume occurs during the assumed maximum rainfall. V B,down (T,R): Flood volume (m 3 ) (Storage section 23 of time-dependent changes in water volume by rainfall intensity)

[0083] Based on the flood analysis assuming the above-mentioned constant rainfall intensity continues, (V B,up or down (T,R)-T B A graph can be created that shows the relationship between ( ). In the flood analysis when the above constant rainfall intensity continues, the vertical axis of this graph is V B,up or down The same amount of water volume V A (T A ) conversion to V A (T A ) to hs A (i,j,T A These correspondences are organized and saved so that they can be converted to (V). This is shown in Figure 6. Here, (V A (T A )orV B,up -T B The graph showing the relationship between (V) is called the "graph for the water level rise period". A (T A )orVB,down -T B The graph showing the relationship between the two is called the "water level decrease period graph." These two graphs are stored in the rainfall intensity-specific water volume time change storage unit 23 so that they can be used in the real-time operation phase. The differences in how to use these two graphs will be explained in detail in the following real-time operation phase. (Real-time operation phase)

[0084] With the inundation depth distribution storage unit 21 and the rainfall intensity-specific water volume time change storage unit 23 prepared as described above, that is, at step S401 of the flowchart in Figure 4, the inundation depth distribution data and rainfall intensity-specific water volume time change data are prepared, and the preparation phase is completed. From step S402 onwards, the actual real-time operation of the inundation depth prediction system begins, i.e., the real-time operation phase. In the real-time operation phase, the inundation depth distribution for an arbitrary rainfall waveform is calculated from the information stored in the "inundation depth distribution storage unit 21" and the "rainfall intensity-specific water volume time change storage unit 23" in the preparation phase, without using a dynamic model. This will be described in detail below. (Input of arbitrary rainfall waveform)

[0085] First, in step S402 of Figure 4, the water volume (V(t)) at the current time step (w) is compared with the time-varying water volume data for different rainfall intensities. Here, the rainfall waveform is given as the input condition for the real-time operation phase. This rainfall waveform assumes the time from the start time to the end time of rainfall, and in this disclosure, Thiessen division is not performed on the watershed, and the watershed average rainfall is used. Basically, the observed and predicted rainfall amounts distributed by the Japan Meteorological Agency are used, with observed rainfall from the start time to the present time, and predicted rainfall from the present time to the end time of rainfall. Except for the exceptions described later, the graph of the water level increase period (graph in Figure 7A and Figure 8) is used during the rainfall increase period of this rainfall waveform, and the graph of the water level decrease period (graph in Figure 7D and Figure 9) is used during the rainfall decrease period, and the basic processing to determine the water volume at the next time step (Figures 8 and 9) is performed. (Considering delays)

[0086] When inputting an arbitrary rainfall waveform in the real-time operation phase, if the use of graphs for the water level increase phase and water level decrease phase is switched at the time when the rainfall waveform transitions from the rainfall increase phase to the rainfall decrease phase, the water level will change from increasing to decreasing without considering the delay time (the difference in peak times between rainfall and inundation). To resolve this problem, even during the rainfall decrease phase, if the inundation volume at the current time step is greater than the steady-state inundation volume, it is assumed that the water level will rise, and the graph for the water level increase phase is used to determine the increase in inundation volume at the next time step using the method described in the calculation of the inundation volume at the next time step (graph in Figure 7B). Similarly, even during the rainfall increase phase, if the inundation volume at an arbitrary time step w is smaller than the steady-state inundation volume, it is assumed that the water level will decrease, and the graph for the water level decrease phase is used to determine the decrease in inundation volume at the next time step (graph in Figure 7C). This allows for the examination of the time change of inundation volume while considering the delay time. (Initial settings for time progression)

[0087] Then, in the input of the arbitrary rainfall waveform for the real-time operation phase described above, the start of rainfall in the rainfall waveform is set as time step w=0, and the real-time operation phase is started. The initial condition is that there is no flood depth (water volume). In this way, in step S402 of Figure 4, the water volume (V(t)) at the current time step (w) is compared with the time change data of water volume by rainfall intensity. (Calculation of the water level at the next time point)

[0088] Next, in step S403, the time change in the amount of water inundated up to the next time step (w+1) (ΔV(R)) is calculated from the time change data of water inundated by rainfall intensity. Here, if it is observed or predicted that a rainfall of R [mm / h] will fall within the step interval Δw from time step w to w+1, the water inundation amount V(w+1) for the next time step is determined by using the graphs for the water level increase period and the water level decrease period, respectively, in the input of the arbitrary rainfall waveform of the real-time operation phase and considering the delay time as described above. In doing so, first the graphs for the water level increase and decrease periods at an arbitrary constant rainfall intensity R [mm / h] (V B,up or down -T BA relationship curve is created between R[mm / h] and R[mm / h] calculated from the time-varying data of inundation volume by rainfall intensity, as shown in Figures 8 and 9. Specifically, the flood analysis when the constant rainfall intensity of the preparatory phase described above continues, and the relationship curve between R[mm / h] and the most recent rainfall intensity R[mm / h] are calculated from the time-varying data of inundation volume by rainfall intensity. const [mm / h] and next-to-next rainfall intensity R const+1 (V) [mm / h] B,up or down -T B The relationship between these two curves is determined, and the weighted average of rainfall intensity is calculated from these two curves to find the relationship between (V) at R[mm / h]. B,up or down -T B Create a relationship curve between the two.

[0089] Next, in step S404, the water volume (V(t+1)) at the next time step (w+1) is estimated based on the time change of the water volume (ΔV(R)). Here, the water volume V(w) at the current time step w and (V B,up or down -T B ) The time T at which the curves intersect B We find the value of time T. B Time T after ΔT time has elapsed B Let +Δw be the time T B +Δw and (V B,up or down -T B The amount of water at which the curves intersect is determined as the amount of water V(w+1) at time step w+1. (Conversion from flood volume to inundation depth distribution)

[0090] Furthermore, in step S405, the inundation volume (V(t+1)) for the time step (w+1) is compared with the inundation depth distribution data. Then, in step S406, the rainfall intensity curve calculation unit calculates the rainfall intensity curve from the rainfall waveform, and further in step S407, the Kadoya-Fukushima formula is calculated, and in step S408, the point where the rainfall intensity curve and the quasi-linear storage function model intersect is calculated as the flood arrival time, and in step S409, the flood arrival time is compared with the inundation depth distribution data to calculate the inundation volume (V(t+1)) for the time step (w+1). A (T A The following steps are performed: )) and in step S410, the average of multiple neighboring points is calculated to determine the averaged inundation depth distribution for the next time step (w+1).

[0091] Specifically, V(w+1) is the amount of water that results in a water volume equivalent to V(w+1). A (T A ) is calculated. Then, hs is defined as the inundation depth distribution in the next time step w+1. A (i,j,T A ) will be output as the result. (Update of specifications for the next time)

[0092] Furthermore, in step S411, it is determined whether the calculation of the averaged flood depth distribution for all time points has been completed. If not, the process returns to step S402 and is repeated.

[0093] Specifically, the parameters are updated from the next time step w+1 to the current time step w, and from V(w+1) to V(w). The calculation of the inundation volume for the next time step in the real-time operation phase, the conversion from the inundation volume to the inundation depth distribution, and the consideration of updating the parameters for the next time step are repeated until the end of the rainfall in the rainfall waveform of the real-time operation phase. [Example 1]

[0094] Next, Figures 10 to 15 show the results of calculating the water volume and flood analysis using the real-time inland flood inundation prediction method according to Embodiment 1. Here, the target area is the Iio River basin (72.0 km²) located in Tokushima City, Ishii Town in Myōzai District, and Yoshinogawa City, Tokushima Prefecture. 2 The study focused on the Iio River. The Iio River originates in Kamojima-cho, Yoshinogawa City, passes through Ishii-cho, enters Tokushima City, and joins the lower reaches of the Ayukui River. The plains, which make up about 70% of the basin, are mostly alluvial plains consisting of floodplains of the Yoshino River, while the southern part of the basin consists of mountains and hills. (Conditions for the preparation phase)

[0095] The conditions for the preparation phase when applying the method of Example 1 are described below. For both the flood analysis using the assumed maximum rainfall as the external force (described later) and the flood analysis under the assumption of continuous constant rainfall intensity, the flood analysis was performed using a 25m x 25m mesh and a calculation time interval of 0.75 [sec]. In the calculations of the preparation phase, each calculation required the same calculation time as the method of Comparative Example 1 in the calculation time comparison described later. If there are changes in land use since the time of the analysis, the flood analysis in the preparation phase needs to be redone accordingly. (Flood analysis using the assumed maximum rainfall as an external force)

[0096] In the flood analysis using the assumed maximum rainfall as the external force, we followed existing guidelines (Ministry of Land, Infrastructure, Transport and Tourism, "Water Management and Land Conservation Bureau: Method for Setting Assumed Maximum External Force for Creating Inundation Predictions (Floods, Inland Waters)," July 2015) and created a centrally concentrated rainfall waveform using the Talbot formula from 1-hour and 24-hour rainfall. We then performed a flood analysis using this waveform as the external force and output the results in 1-hour increments. (Flood analysis under the assumption of sustained rainfall intensity)

[0097] In the flood analysis under the assumption of continuous constant rainfall intensity, to create the graph for the water level rise phase, rainfall forces were created for continuous constant rainfall intensities of 10, 20, 30, 40, 50, 70, 90, 110, 150, 185, and 220 [mm / h]. This considers the range from 10 [mm / h], where inundation begins, to the assumed maximum hourly rainfall of 220 [mm / h]. To create the graph for the water level decrease phase, rainfall forces were created for continuous constant rainfall intensities of 0, 3, 6, 9, 12, 15, and 20 [mm / h]. This is because, under initial conditions where inundation is occurring, continuous rainfall of 20 [mm / h] or more exceeds the drainage capacity in the target watershed, causing the water level to rise. A flood analysis was performed using these forces, and the results were output in one-hour increments. (Application results and verification)

[0098] The Tokushima Local Meteorological Observatory used the heavy rainfall of September 20, 2011 (429.5 mm / day), which had a high daily rainfall in recent years, as the external force, and compared the results of flood analysis using the method of Comparative Example 1 and the method of Example 1. In this heavy rainfall, although the locations of inundation could not be identified in the Iio River basin, 34 houses were confirmed to have experienced above-floor flooding according to flood statistics (Water Management and Land Conservation Bureau, Ministry of Land, Infrastructure, Transport and Tourism, "2011 Flood Statistics, Basic Table of Flood Statistics, Basic Table of Flood Statistics for General Assets, etc." April 2015). (Temporal changes in water volume)

[0099] Comparing the time-dependent changes in the water volume using the method in Comparative Example 1 and the method in Example 1, it was confirmed that the increase or decrease due to time and the water volume were in roughly agreement, as shown in Figure 10. (Maximum immersion depth and maximum flux flow)

[0100] Next, in order to evaluate the flooding above floor level, the method of Example 1 was verified based on the maximum flood depth and the maximum value of the flow flux, which is of equal importance to the flood depth in two-dimensional planar unsteady flow analysis. Comparing the maximum flood depth of the method of Comparative Example 1 with that of Example 1, it was confirmed that most of the flood depth values ​​were plotted near the y=x line, as shown in Figure 11.

[0101] Furthermore, in considering the maximum inundation depth, maximum flow flux, and the time of occurrence of maximum inundation depth and above-floor flooding, as well as the inundation depth every hour on the hour, as described later, the meshes in which an inundation depth of 0.5 [m] or more (the indicator of above-floor flooding, as stated in the "Guidelines for Formulating Inland Water Treatment Plans" by the National Land Development Technology Research Center, p. 114, February 1995) occurred using the method of Comparative Example 1. The correlation coefficient (r) was 0.997, indicating a very strong correlation between the two methods. The average difference in maximum inundation depth between the two methods was 0.031 [m]. This is 4% of the average maximum inundation depth of 0.757 [m]. Also, the average difference in inundation depth between the two methods is 6% of the indicator of above-floor flooding of 0.5 [m], suggesting that the distribution of maximum inundation depths can be evaluated. In the method of Comparative Example 1, the location where an inundation depth of 0.5 [m] occurred (1.30 km 2 82% of these locations (1.07km) experienced a flood depth of 0.5m even with the method of Example 1. 2It was determined that... Therefore, the method of Example 1 is considered to be able to evaluate whether or not flooding above floor level has occurred.

[0102] Comparing the maximum flow flux of the Comparative Example 1 method and the Example 1 method (Figure 12), it can be seen that most of the flow flux values ​​are plotted near the y=x line, and that the flow flux of the Comparative Example 1 method is larger. This is thought to be because, as described later as the application scope of Embodiment 1, the inundation depth of the Example 1 method is located upstream of the area where the inundation depth is distributed, resulting in a greater flow velocity in the Comparative Example 1 method than in the Example 1 method. On the other hand, in the region of low maximum flow flux in Figure 12, the Example 1 method has a larger maximum flow flux than the Comparative Example 1 method. The flood analysis model of Embodiment 1 adds rainfall to the water depth, and the model assumes that the inundated water flows down to lower elevation areas. The flow flux in this region was observed in areas where the elevation is relatively higher than the surrounding area and the water depth is smaller than the surrounding area. In such areas, the flow velocity of the Comparative Example 1 method is larger than that of the Example 1 method, so the water flows downstream before the water depth increases. On the other hand, the method of Example 1 has a lower flow velocity than the method of Comparative Example 1, so it does not flow downstream until the water depth increases. Since the flow flux is the product of the water depth and the flow velocity, although the flow velocity of the method of Example 1 is lower than that of the method of Comparative Example 1, it is thought that the maximum flow flux of the method of Example 1 was calculated to be larger than that of the method of Comparative Example 1 because the water depth is greater than the flow velocity. The correlation coefficient (r) was 0.914, indicating a very strong correlation between the two methods. Also, the average difference in flow flux between the two methods was 0.038 [m 2 The value was [ / s]. This is the average value of the maximum flow flux, 0.118 [m³]. 2 It was found that the reproducibility was 32% of [ / s], and that it tended to be less reproducible than the maximum flood depth. (Maximum flood depth / Time of flooding above floor level)

[0103] The average time of maximum flood depth occurrence obtained for each mesh was 22:59 on the 20th using the method of Comparative Example 1, and 23:33 using the method of Example 1. It was found that Comparative Example 1 was 34 minutes earlier than Example 1. Also, the average time of above-floor flooding occurrence obtained for each mesh was 14:00 on the 20th for Comparative Example 1, and 14:35 for Example 1. It was found that Comparative Example 1 was 35 minutes earlier than Example 1. (Flood depth every hour on the hour)

[0104] As mentioned above, the time progression in Example 1 was slower than in Comparative Example 1 for the maximum inundation depth and the time of flooding above floor level. Therefore, we examined the inundation depth at arbitrary times. We output data for 72 hours from 0:00 on September 19th to 24:00 on September 21st at one-hour intervals, and plotted the inundation depths of both methods at locations where the inundation depth of 0.5 [m] or more occurred using the Comparative Example 1 method (Figure 13). Most of the inundation depth values ​​were plotted below the y=x line, confirming that Example 1 tends to have smaller inundation depths. This is thought to be because the time progression in Example 1 is slower than in Comparative Example 1 for the maximum inundation depth and the time of flooding above floor level. As a result, the floodwaters in Example 1 collect more slowly than in Comparative Example 1, leading to a lower calculated inundation depth. Therefore, the correlation coefficient (r) was 0.761, which is smaller than when examining the maximum inundation depth. The average difference in inundation depth between the two methods was 26.3 [cm]. This is 53% of the 0.5m threshold used as an indicator of flooding above floor level, and it is difficult to say that the two methods are consistent. Therefore, it is considered that the flood depth at any given time cannot be calculated. (Flood depth distribution as of 11:00 PM on the 20th)

[0105] Comparing the flood depth distribution at 23:00 on the 20th using the method of Comparative Example 1 (Figure 14) with that of the method of Example 1 (Figure 15), it was confirmed that they were generally in agreement. Comparing these figures, it can be seen that, although slightly, the flood depth is greater in Comparative Example 1 than in Example 1 at the locations indicated by the arrows in the enlarged views of both figures. Comparing the average time of maximum flood depth occurrence at 23:00 on the 20th with the average time of maximum flood depth occurrence for both methods, the difference is 1 minute for Comparative Example 1, while the difference is 33 minutes for Example 1, indicating a larger time difference. This is considered to be the reason why the flood depth is greater in Comparative Example 1 than in Example 1. (Scope of application of Example 1)

[0106] While the method in Example 1 can calculate the time-dependent change in the amount of water inundated, it cannot determine the inundation depth at an arbitrary time. This is because, when converting from the amount of water inundated to the inundation depth distribution ("(7) Conversion" in the inundation depth distribution storage section 21 in Figure 3), it is converted to an inundation depth distribution during the water level rise period calculated using the assumed maximum rainfall as an external force. As a result, the inundation depth is positioned further upstream than the location where the inundation depth is distributed in Comparative Example 1. However, although the time progression in Example 1 is slower than in Comparative Example 1, if the calculation is performed from the time the inundation occurs until it is resolved, it is thought that the floodwaters will eventually reach the downstream end of the basin, and the same maximum inundation depth and locations of above-floor inundation as in Comparative Example 1 can be obtained. (Comparison of calculation times)

[0107] The specifications of the personal computer used for the calculations were an Intel® Core® i7-8700K CPU at 3.70GHz and 64.00GB of RAM. In the flood analysis of Comparative Example 1 (target time 72[h]), the calculation time was 25 hours and 6 minutes, while in the real-time operation phase of Example 1, the calculation time was 1 minute and 12 seconds. [Example 2]

[0108] As described above, the real-time inland flood inundation prediction system according to Example 1 utilizes a statistical model to achieve real-time inland flood prediction, which was difficult to achieve with calculations using dynamic models during operation. On the other hand, in Example 1, the only indicator used to evaluate the inundation depth distribution was the amount of water inundated, which is a property of the continuous equation. Therefore, as Example 2, a real-time inland flood inundation prediction method is described below, which evaluates the inundation depth distribution using flood arrival time as a property of the equation of motion, in addition to the amount of water inundated. Specifically, the system is separated into a pre-preparation phase and a real-time operation phase, and in the real-time operation phase, the inundation depth distribution is retrieved by considering two indicators: the amount of water inundated and the flood arrival time. This will be described in detail below. (Flood arrival time)

[0109] Figure 16 shows an overview of the real-time inland flood inundation prediction method according to Example 2. Steps (1) to (6) are the same as in Example 1 shown in Figure 3. (1) The inundation depth distribution storage unit 21 and the rainfall intensity-specific water volume time change storage unit 23 are prepared in advance. (2) The water volume (V(t)) for the current time step (w), which is to be a known quantity, is transferred to the rainfall intensity-specific water volume time change storage unit 23. (3) The time change amount (ΔV(R)) of the water volume when an arbitrary rainfall amount (R) occurs is calculated. (4) The water volume (V(t+1)) for the next time step (w+1) is estimated. (5) The water volume (V(t+1)) for the next time step (w+1) is transferred to the inundation depth distribution storage unit 21. (6) The water volume (V A (T A )) is received. The difference from Example 1 is the addition of steps (7) and (8) of the flood arrival time calculation unit 33.

[0110] In this disclosure, the flood arrival time is calculated using tc: flood arrival time [hr] as published on page 94 of "Revised New Edition of the Ministry of Construction's River and Erosion Control Technical Standards (Draft) and Commentary, Survey Edition," supervised by the River Bureau of the Ministry of Construction and edited by the Japan River Association, Gihodo Publishing (September 1997). According to the same document, tc is determined from the empirical formula for flood arrival time by Kadoya et al. and the effective rainfall intensity curve of actual data.

[0111] tc=C·A 0.22·re -0.35 however, re: Maximum mean effective rainfall intensity [mm / h] during the duration of rainfall; A: Basin area [km 2 ]; tc: flood arrival time; C: A constant determined by the land use pattern; (In this disclosure, the above formula is referred to as the "Kadoya-Fukushima formula.") (Flood arrival time calculation unit 33)

[0112] The flood arrival time calculation unit 33 calculates the flood arrival time (T) at the time of the next time step (w+1). P_A The flood arrival time is calculated in accordance with the quasi-linear storage function model described in Chapter 3, Section 2, p. 19 (June 2012) of the "River and Erosion Control Technical Standards, Survey Edition," Water Management and Land Conservation Bureau, Ministry of Land, Infrastructure, Transport and Tourism. Finally, the inundation depth is determined by interpolating pre-calculated nearby known values ​​from the two indicators, the inundation volume (6) and the flood arrival time (8), and the distribution of these inundation depths is displayed. (Preparation Phase)

[0113] In the preparation phase, the flood analysis using the assumed maximum rainfall stored in the inundation depth distribution storage unit 21 as the external force differs from that in Example 1. In addition to flood analysis using the assumed maximum rainfall (Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land Conservation Bureau, "Method for Setting Assumed Maximum External Forces for Creating Inundation Predictions (Floods, Inland Waters): p.10 (July 2015)") as the external force, the inundation depth distribution storage unit 21 also performs flood analysis using 2-year, 10-year, 50-year, 100-year, and 500-year probability rainfall as the external force. For these six probability years, flood analysis was performed using forward-concentrated, central-concentrated, and backward-concentrated rainfall waveforms. The results of these 18 cases (= 6 rainfall probabilities × 3 rainfall waveforms) (hereinafter referred to as "results of 18 cases of assumed maximum rainfall, etc."), namely the inundation volume and flood arrival time, are stored in the inundation depth distribution storage unit 21. The flood analysis performed when a constant rainfall intensity is sustained, and the rainfall intensity-specific water volume time change storage unit 23 that stores the results, stored the time changes of the water volume in the same manner as in Example 1. (Real-time operation phase)

[0114] In the real-time operation phase, the inundation depth distribution for an arbitrary rainfall waveform is calculated without using a dynamic model, based on the information stored in the inundation depth distribution storage unit 21 and the rainfall intensity-specific inundation volume time change storage unit 23 during the preparation phase. This procedure is shown in the flowchart of Figure 17. Steps S1701 to S1704 are the same as in Example 1, and a detailed explanation is omitted. Steps S1705 to S1707 will be explained below. (Calculation of flood arrival time)

[0115] In step S1705 of the flowchart in Figure 17, the flood arrival time is calculated. The flood arrival time is determined in accordance with the quasi-linear storage function model (Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land Conservation Bureau, "River and Erosion Control Technical Standards, Survey Edition," Chapter 3, Section 2, p. 19 (June 2012)), and is the value at which the Kadoya-Fukushima formula intersects with the rainfall intensity curve. The obtained results are passed from the flood arrival calculation unit to the inundation depth distribution preservation unit 21. In the quasi-linear storage function, the rainfall intensity was converted to effective rainfall and used in both the rainfall intensity formula and the quasi-linear storage function model. (The distribution of inundation depth is calculated from the water volume and the time of flood arrival.)

[0116] Next, in step S1706 of the flowchart in Figure 17, the inundation depth distribution is calculated from the water volume and flood arrival time. The concept of calculating the inundation depth in the time step is shown in Figure 18. In this figure, the plots in the time step are shown with crosshatching, the results of 18 assumed maximum rainfall cases are plotted in light gray, the results of the 18 assumed maximum rainfall cases closest to the crosshatch in each quadrant are plotted in dark gray, and the inundation depth distribution without water volume is plotted with diagonal lines. As shown in this figure, the results of 18 assumed maximum rainfall cases for any time and any mesh are plotted in light gray on a graph with water volume on the vertical axis and flood arrival time on the horizontal axis. Here, since the units of the vertical and horizontal axes are different, the distances between the vertical and horizontal axes are made equal by normalization (converting to standard scores). Here, in order to determine the inundation depth in the next time step, we take the plot to be calculated as the origin and set the vertical and horizontal axes accordingly. In the first to fourth quadrants, we select the results for the 18 nearest assumed maximum rainfall cases from this plot. The nearest results can be determined by normalization, which allows us to calculate the distance on a two-dimensional plane. Then, we find the ratio of the distances to the points where the line connecting the neighboring points of the second and third quadrants intersects the horizontal axis (R2:R3). Similarly, we find the ratio of the distances to the points where the line connecting the neighboring points of the first and fourth quadrants intersects the horizontal axis (R1:R4). We find the ratio of the distances to the origin between the points where the line connecting the neighboring points of the second and third quadrants intersects the horizontal axis and the points where the line connecting the neighboring points of the first and fourth quadrants intersects the horizontal axis (R23:R14). Using the following equation 6, we interpolate the neighboring points of each of the four quadrants to determine the inundation depth in the next time step. The inundation depth distribution is calculated by repeating these procedures in each mesh. Furthermore, if there are no plots of the calculation results for the 18 assumed maximum rainfall cases in quadrants III and IV, the flood arrival time is equivalent to the cross-hatch pattern, and plots are placed in locations where there is no inundation volume (inundation depth distribution of 0.0 [m]) (the shaded points in Figure 18 are placed, and the dark gray plots of the calculation results for the 18 assumed maximum rainfall cases in quadrants III and IV that are closest to the cross-hatch plot in the next time step are used as substitutes). Example 2 deals with inland flooding, and level inundation in flat areas is assumed.Therefore, since it is more important to evaluate the water volume in detail than the water surface gradient, we first evaluated the water volume, which is an indicator of water volume, and then evaluated the flood arrival time, which is an indicator of water surface gradient.

[0117] [Number 6] TIFF2026059098000007.tif34147

[0118] Here i,j: Grid numbers in the latitude and longitude directions for flood analysis [-]; T A, : The time interval of the rainfall waveform for the assumed maximum rainfall, and the elapsed time [h] corresponding to the next step in the flood analysis using the rainfall waveforms for 18 cases, including the assumed maximum rainfall, as external forces; R2:R3: The ratio of the distances to the points where the line connecting neighboring points in the second and third quadrants intersects the horizontal axis [-]: R1:R4: The ratio of the distances to the points where the lines connecting neighboring points in the first and fourth quadrants intersect the horizontal axis [-]; R23:R14: The ratio of the distance from the origin between the point where the line connecting the neighboring points of the second and third quadrants intersects the horizontal axis and the point where the line connecting the neighboring points of the first and fourth quadrants intersects the horizontal axis.[-] hs(i,j,T A ): The immersion depth [m] of grid number (i,j) in the next time step; hs1~4(i,j,T A ): The resulting inundation depth [m] for 18 cases of assumed maximum rainfall, etc., in the I-IV quadrants of grid number (i,j) in the time step; That is the case. (Update of specifications for the next time)

[0119] Finally, in step S1707 of the flowchart in Figure 17, the parameters for the next time step are updated. The parameters are updated from the next time step w+1 to the current time step w, and from V(w+1) to V(w). The process from steps S1704 to S1707 is repeated until the end of the rainfall in the rainfall waveform of step S1701. (Application and verification of Example 2) (Target area)

[0120] In Example 2, the Iio River basin (72.0 km²) is located in Tokushima City, Ishii Town in Myōzai District, and Yoshinogawa City, Tokushima Prefecture. 2 The study focused on the Iio River. The Iio River originates in Kamojima-cho, Yoshinogawa City, passes through Ishii-cho, enters Tokushima City, and joins the lower reaches of the Ayukui River. The plains, which make up about 70% of the basin, are mostly alluvial plains consisting of floodplains of the Yoshino River, while the southern part of the basin consists of mountains and hills. (Conditions for the preparation phase)

[0121] In the following two flood analyses, the calculation mesh was 25[m]×25[m] and the calculation time interval was 0.75[sec]. The calculation time required for the preparation phase was the same as in Example 1. (Flood analysis based on 18 scenarios including assumed maximum rainfall)

[0122] The assumed maximum rainfall was calculated using the Talbot formula from hourly and 24-hour rainfall, in accordance with the Ministry of Land, Infrastructure, Transport and Tourism guidelines (Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land Conservation Bureau, "Method for Setting Assumed Maximum External Forces for Creating Inundation Predictions (Floods, Inland Waters)," p. 10 (July 2015)). Other probability rainfalls (2-year probability, 10-year probability, 50-year probability, 100-year probability, 500-year probability) were created using the rainfall intensity formula described in the Tokushima Prefecture guidelines (Tokushima Prefecture Land Development Department, "Rivers and Coasts of Tokushima Prefecture," p. 143 (September 2022)). For these six probability rainfall cases, flood analysis was performed using forward-concentrated rainfall waveforms, central-concentrated rainfall waveforms, and backward-concentrated rainfall waveforms as external forces, and the results were output in one-hour increments. (Flood analysis under the assumption of sustained rainfall intensity)

[0123] In creating the graph for the water level rise period, similar to Example 1, we created rainfall forces assuming a continuous constant rainfall intensity of 10, 20, 30, 40, 50, 70, 90, 110, 150, 185, and 220 [mm / h]. This takes into account the range from 10 [mm / h], when flooding begins, to the assumed maximum hourly rainfall of 220 [mm / h] (Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land Conservation Bureau, "Method for Setting Assumed Maximum External Forces for Creating Flood Inundation Predictions (Floods, Inland Waters)," p. 10 (July 2015)). On the other hand, in creating the graph for the water level fall period, similar to Example 1, we created rainfall forces assuming a continuous constant rainfall intensity of 0, 3, 6, 9, 12, 15, and 20 [mm / h]. This is because, under initial conditions where flooding is occurring, continuous rainfall of 20 [mm / h] or more exceeds the drainage capacity of the target watershed, causing the water level to rise. The difference from Example 1 is that the flood analysis results, using these as external forces, were output in 5-minute intervals. This improved the applicability when rapid temporal changes occur in the rainfall waveform during the real-time operation phase. (Application results and verification)

[0124] Here, we used the results of a two-dimensional planar unsteady flow calculation using the heavy rainfall of September 20, 2011 (429.5 mm / day), which is considered to have high daily rainfall in recent years, as the external force for flood analysis (Manabu Miyoshi, Takao Tamura, Hironori Muto, Hiroshi Aki, "An Inland Water Analysis Model for Creating Wide-Area and Detailed Inundation Depth Distribution Using Open Data Available Nationwide," Journal of Hydraulic Engineering, Vol. 74, No. 4, pp. I 1321-I 1326 (2018)) as Comparative Example 2, and compared it with the results output by Example 2 (time change in inundation volume, maximum inundation depth, maximum flow flux, etc.). In Example 1, we focused on the flood depth of above-floor flooding (0.50m or more) and verified the method used in Example 2. In Example 2, when evaluating above-floor flooding, we added the display of a flood rank color scheme in accordance with the "Guidelines for Creating Flood Hazard Maps" (Water Management and Land Conservation Bureau, Ministry of Land, Infrastructure, Transport and Tourism, p.36 (revised December 2021)) to the scope of verification.

[0125] During the heavy rainfall on September 20, 2011 (429.5 mm / day), although the exact locations of flooding could not be identified in the Iio River basin, 34 houses were confirmed to have experienced above-floor flooding according to flood statistics (Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land Conservation Bureau, "2011 Flood Statistics, Basic Flood Statistics Table, Basic Flood Statistics Table for General Assets, etc." April 2015). In Example 2, the 5m [m] base map information digital elevation model was used for both the flood analysis in Example 2 and Comparative Example 2. (Temporal changes in water volume)

[0126] Comparing the time-dependent changes in the water volume of Comparative Example 2 and Example 2, it was found that the increase or decrease due to time and the water volume were roughly in agreement, as shown in Figure 19. (Maximum immersion depth and maximum flux flow)

[0127] To evaluate the level of flooding above floor level, Example 2 was verified based on the maximum flood depth and the maximum value of the flow flux, which is of equal importance to the flood depth in two-dimensional planar unsteady flow analysis. The flow flux was calculated by multiplying the flow velocity, which was obtained in the same way as the flood depth, by the flood depth.

[0128] Comparing the maximum inundation depths of Comparative Example 2 and Example 2, it was found that most of the inundation depth values ​​were plotted near the y=x line, as shown in Figure 20. For the analysis of maximum inundation depth, maximum flow flux, and the time of occurrence of maximum inundation depth and above-floor inundation (described later), as well as the inundation depth every hour, the meshes where inundation depths of 0.5 [m] or more (the index for above-floor inundation, as stated in the "Guidelines for Formulating Inland Water Treatment Plans" by the National Land Development Technology Research Center, p. 114 (February 1995)) occurred using the method of Comparative Example 2 were targeted. The correlation coefficient (r) was 0.994, indicating a very strong correlation between the two methods. The average difference in maximum inundation depth between the two methods was 0.030 [m], meaning Example 2 had an error of 3.81% compared to Comparative Example 2. Furthermore, the average difference in inundation depth between the two methods is 6% of 0.5 [m], the index for above-floor inundation, suggesting that the maximum inundation depth distribution can be evaluated. In the method of Comparative Example 2, the number of locations where flooding depths of 0.5 [m] or more occurred (2.66 km) 2 ) 91% of these were locations where flooding depths of 0.5 [m] or more occurred in Example 2 (2.41 km) 2) was determined. Therefore, it is considered that Example 2 can be used to evaluate whether or not flooding above floor level occurred.

[0129] Furthermore, comparing the maximum flow flux of Comparative Example 2 and Example 2, it was found that most of the flow flux values ​​were plotted near the y=x line, as shown in Figure 21. The correlation coefficient (r) was 0.982, indicating a very strong correlation between the two methods. The average difference in maximum flow flux between the two methods was 0.014 [m 2 The value was [ / s], and Example 2 had a 27.77% error compared to the method of Comparative Example 2. Furthermore, while a tendency for Comparative Example 2 to have a larger flow flux was observed in Example 1, in Example 2, the flood arrival time was considered when calculating the inundation depth distribution, so the tendency for Comparative Example 2's method to have a larger flow flux disappeared. From these findings, it can be concluded that the maximum flow flux achieved the same level of reproducibility as the maximum inundation depth. This improvement in reproducibility is thought to be due to the elimination of the time progression difference caused by considering the flood arrival time. In other words, it is unlikely that this was resolved by spatial factors such as using a 5[m] base map data elevation model instead of a 10[m] base map data model when creating the ground elevation model. (Time of maximum flood depth and time of flooding above floor level)

[0130] The average time of maximum inundation depth obtained for each mesh was 20:35 on the 20th using the method of Comparative Example 2, and 20:27 in Example 2. Comparative Example 2 was 8 minutes earlier than Example 2, indicating that the time difference is not problematic when recommending a time for residents to evacuate in advance on the day of heavy rain. Furthermore, the average time of above-floor inundation obtained for each mesh was 12:38 on the 20th for Comparative Example 2, and 12:23 for Example 2. Comparative Example 2 was 15 minutes earlier than Example 2, indicating that the time difference is not problematic when recommending a time for residents to evacuate in advance on the day of heavy rain. Note that these times were calculated in minutes and then the spatial average was taken. (Flood depth every hour on the hour)

[0131] The above-mentioned times of maximum flood depth occurrence and floor-level flooding occurrence suggest that the time progression in Comparative Example 2 and Example 2 may be equivalent; therefore, we examined the flood depth at arbitrary times. We output data for 72 hours from 0:00 on September 19th to 24:00 on September 21st at one-hour intervals, and Figure 22 shows a graph plotting the flood depths of both methods at locations where a flood depth of 0.5 [m] or more occurred in Comparative Example 2 at these times. As shown in this figure, although there is a tendency for the data to be plotted along the y=x line, there is more variation relative to the y=x line than in the plots used for comparing maximum flood depths in Figure 21. However, the tendency for Example 2 to have smaller flood depths than Comparative Example 2, as seen in Example 1, was eliminated. The correlation coefficient (r) was 0.934, indicating a very strong correlation between the two methods. The average difference in flood depth at one-hour intervals between the two methods was 0.121 [m], which was a 13.72% error compared to the method of Comparative Example 2. Furthermore, the average difference in flood depth between the two methods is 24% of 0.5 [m], which is considered an indicator of flooding above floor level, suggesting that it captures the trend of changes in flood depth over time. (Accuracy of color schemes in flood hazard maps)

[0132] The Ministry of Land, Infrastructure, Transport and Tourism's Water Management and Land Conservation Bureau's "Guidelines for Creating Flood Hazard Maps" uses inundation depths of 0.3, 0.5, 1.0, 3.0, 5.0, 10.0, and 20.0 [m] as thresholds, and the color scheme for displaying inundation depth changes at these thresholds. In Example 2, the percentage of cases where the color scheme is the same in both Comparative Example 2 and Example 2 is defined as the color scheme accuracy rate. In both Comparative Example 2 and Example 2, cases with no inundation depth were excluded from the color scheme accuracy rate calculation. Figure 23 shows the color scheme accuracy rate when the inundation depth distribution is displayed every hour on the hour. As shown in this figure, the color scheme accuracy rate is small at the beginning and end of light rainfall, but it increases as the amount of water increases along with the amount of rainfall. This is thought to be because the range of color scheme thresholds is set finely when the inundation is small, but the range of color scheme thresholds widens as the inundation depth increases. On the other hand, a total of 1,994,310 meshes experienced flooding every hour on the hour during the calculation period (72 hours). Of these, the color scheme was accurate for 1,567,580 meshes, resulting in a color scheme accuracy rate of 79% throughout the calculation period (72 hours). Therefore, it is considered that displaying the map using the color scheme outlined in the "Guidelines for Creating Flood Hazard Maps" allows for a general assessment of the flood depth distribution over time (especially in cases of high flood depth). Furthermore, when calculating the color scheme accuracy rates for Comparative Example 2 and Example 2 in the maximum flood depth distribution, the color scheme was accurate for 69,474 meshes out of 74,357 meshes where flooding occurred. Therefore, the color scheme accuracy rate was 93%, suggesting that displaying the map using the color scheme outlined in the "Guidelines for Creating Flood Hazard Maps" allows for a proper assessment of the maximum flood depth distribution.

[0133] Figure 24 shows the maximum inundation depth distribution using the method of Comparative Example 2, and Figure 25 shows the maximum inundation depth distribution using Example 2, when displayed with the color scheme specified in the "Guidelines for Creating Flood Hazard Maps." From these figures, it can be seen that both methods are generally in agreement. Note that Figures 24 and 25 were created with different color schemes than those specified in the "Guidelines for Creating Flood Hazard Maps," although the threshold values ​​for the color scheme are the same, in order to make the colors easier to see. Looking at the enlarged views of Figures 24 and 25, it can be seen that the inundation depth tends to be greater in fields and rivers that are one level lower than houses, and the color schemes for these inundation depths are also similar. Therefore, it is considered appropriate to follow the color scheme of the "Guidelines for Creating Flood Hazard Maps" as the display method when evaluating flooding above floor level in the future. On the other hand, Figure 26 shows the difference in maximum inundation depth between the two methods. Looking at this figure, the difference is small in storage-type flooding that occurs in fields, but the difference tends to be large in flow-type flooding that occurs in valleys and rivers. This is likely because, in storage-type floods, the water depth will match in both methods if the water volume is the same, but in flow-type floods, it is difficult to make the relationship between water depth and flow velocity match in both methods. (Accuracy rate of locations where flooding occurred above floor level)

[0134] In the case of areas where flooding occurred above floor level using the method of Comparative Example 2 (flood depth of 0.5m or more (Japan Land Development Technology Research Center, "Guidelines for Formulating Inland Water Treatment Plans," p. 114 (February 1995))), the accuracy rate of predicting flooding above floor level in Example 2 is defined as the flooding above floor level accuracy rate in Example 2. In the method of Comparative Example 2, cases where no flooding occurred were excluded from the flooding above floor level accuracy rate. Figure 27 shows the flooding above floor level accuracy rate for every hour on the hour during the calculation period (72 hours). This figure shows that the accuracy rate was over 97% immediately after the flooding occurred. There were a total of 2,004,099 meshes where flooding above floor level occurred at each hour on the hour. Of these, 1,981,022 meshes were predicted to have flooding above floor level, and the flooding above floor level accuracy rate throughout the calculation period (72 hours) was 99%. Therefore, it is considered that flooding above floor level can be evaluated over time. (Accuracy of color schemes at the time when flooding begins)

[0135] Figure 28 shows the inundation depth distribution using the method of Comparative Example 2, and Figure 29 shows the inundation depth distribution using the method of Example 2, at the time when flooding above floor level began (17:00 on the 19th). Comparing the two methods, it can be seen that the locations where flooding occurs in Example 2 are different from those in Comparative Example 2. This is because, as shown in Figure 18, the inundation depth distribution in Example 2 is calculated by interpolating the results of flood analysis for 18 cases of assumed maximum rainfall, etc. Therefore, when the flooding is small and the flooded areas before collection are scattered, the locations where flooding occurs differ between the two methods, which is thought to be the reason why the accuracy of predicting flooding above floor level is low. (Comparison of calculation times)

[0136] The specifications of the personal computer used in the calculations for Example 2 and Comparative Example 2 were an Intel® Core® i7-8700K CPU @ 3.70GHz and 64.00GB of RAM. The flood analysis in Comparative Example 2 (target time 72h) required 25 hours and 14 minutes of calculation time, while the actual operation phase of Example 2 required 2 minutes and 49 seconds of calculation time. Therefore, it is considered that Example 2 can reduce the calculation time by 0.19%.

[0137] As described above, Example 2 realizes a real-time inland flood inundation prediction method that evaluates the inundation depth distribution from the time of flood arrival in addition to the amount of water inundated, making it possible to evaluate the inundation depth distribution using the amount of water inundated and the time of flood arrival. In Example 1, an attempt was made to evaluate the inundation depth distribution using only the amount of water inundated as an indicator, but it was not possible to calculate the inundation depth at an arbitrary time and the time when floor-level inundation occurs. In Example 2, when the inundation depth distribution was evaluated using the time of flood arrival as an indicator in addition to the amount of water inundated, it was considered that the trend of the time change in the inundation depth at an arbitrary time was captured. Therefore, it was considered that the inundation depth distribution can be evaluated using the amount of water inundated and the time of flood arrival.

[0138] Furthermore, Example 2 allows for the evaluation of the temporal changes in water volume, the distribution of maximum inundation depth, the time of on-floor inundation, and the location of on-floor inundation, thereby enabling the assessment of on-floor inundation. In addition, when the distribution of inundation depth at an arbitrary time was displayed using the color scheme prescribed in the "Guidelines for Creating Flood Hazard Maps," the accuracy rate of the color scheme was 79%. Thus, it is considered appropriate to follow the color scheme prescribed in the said guidelines for calculating on-floor inundation and displaying the distribution of inundation depth in Example 2.

[0139] Furthermore, according to Example 2, the calculation time was reduced from 25 hours and 14 minutes to 2 minutes and 49 seconds (0.19%). In particular, the accuracy of the method for predicting inland flooding in real time is thought to be due to the time required for evacuation lead time in local communities. It is considered necessary to have accurate information distribution and calculation results at time intervals that allow for appropriate evaluation of the time required for that lead time. On the other hand, Example 2 can evaluate the time change of the amount of inundation, the distribution of the maximum inundation depth, the time of occurrence of above-floor inundation, and the location of above-floor inundation. However, Example 2 may not be able to evaluate the phenomenon in which inundation in the upstream area flows down to the downstream area and floods the downstream area in a wide watershed where there is rainfall in the upstream area but no rainfall in the downstream area. Therefore, in a wide watershed, it is preferable to perform a fine-mesh flood analysis in advance, such as the 18 cases of assumed maximum rainfall etc. in Example 2. Then, flood analysis is performed over a wide watershed using a mesh coarse enough to allow for real-time calculations, and the inundation volume and flood arrival time for each of these coarse meshes are calculated in real-time operation. By using these two indicators—inundation volume and flood arrival time—to retrieve the inundation depth distribution for finer meshes, it becomes possible to predict inland flooding even in wide watersheds where there is rainfall in the upstream areas but no rainfall in the downstream areas. [Industrial applicability]

[0140] The real-time inland flood inundation prediction system, real-time inland flood inundation prediction device, real-time inland flood inundation prediction method, real-time inland flood inundation prediction program, computer-readable recording medium, and stored device described herein will enable inland flood inundation prediction. This will allow for accurate prediction of inland flooding in urban areas during super typhoons, linear rainbands, etc., and will be useful in creating evacuation routes. [Explanation of Symbols]

[0141] 1000, 1000'... Real-time inland flood inundation prediction system 100...Real-time inland flood inundation prediction device 10...Data acquisition unit 20...Data storage unit 21…Flood depth distribution preservation section 22…Predicted Inundation Depth Distribution Model Storage Section 23…Storage section for time-dependent changes in floodwater volume by rainfall intensity 26...Rainfall intensity curve storage section 27... Quasi-linear storage type function model preservation unit 30...Arithmetic section 31...Flooding amount calculation section 32…Database Access Section 33…Flood arrival time calculation section 34…Predicted flood depth distribution model extraction unit 35... Quasi-linear storage-type function model calculation unit 40...Display section 50...Operation unit CL...Client device WS...Water level sensor RT... Repeater

Claims

1. A real-time inland flood inundation prediction system that predicts inland flooding in a predicted area caused by rainfall in real time, under the conditions that the predicted area is divided into small watersheds, uniform rainfall occurs within each small watershed, and a pre-calculated inundation depth distribution occurs at each location within the predicted area for each small watershed, A data acquisition unit that acquires observed rainfall and predicted rainfall, A water level calculation unit calculates the current water level in real time based on the observed rainfall acquired by the data acquisition unit, the predicted rainfall, and the initial water level. A predicted inundation depth distribution model storage unit for storing multiple predicted inundation depth distribution models that have been pre-calculated for each inundation volume in the predicted area, A flood arrival time calculation unit for calculating the expected flood arrival time in the predicted area based on rainfall intensity, A predicted inundation depth distribution model extraction unit extracts a predicted inundation depth distribution model that corresponds to the corrected inundation depth, which is calculated in real time by the inundation volume calculation unit, taking into account the flood arrival time calculated by the flood arrival time calculation unit, and extracts a predicted inundation depth distribution model from among a plurality of predicted inundation depth distribution models stored in the predicted inundation depth distribution model storage unit, the inundation volume calculated in real time by the inundation volume calculation unit, and A display unit for displaying the predicted inundation depth distribution model extracted by the predicted inundation depth distribution model extraction unit, A real-time inland flood inundation prediction system equipped with the following features.

2. A real-time inland flood inundation prediction system according to claim 1, further comprising: A rainfall intensity-based flood volume time change storage unit stores the results of pre-calculated flood analysis that shows the relationship between flood volume and elapsed time according to rainfall intensity, A section for preserving the inundation depth distribution to preserve the relationship between flood arrival time and water volume, A real-time inland flood inundation prediction system equipped with the following features.

3. A real-time inland flood inundation prediction system according to claim 2, The aforementioned storage unit for the time-dependent change in water volume according to rainfall intensity, As a graph of the water level rise period, the flood analysis results were obtained when the rainfall force was assumed to be the continuous constant rainfall intensity of multiple different intensities, As a graph of the water level decrease period, the flood analysis results were obtained when the rainfall force was assumed to be the continuous constant rainfall intensity of multiple different intensities, A real-time inland flood inundation prediction system that includes this at predetermined intervals.

4. A real-time inland flood inundation prediction system according to claim 2, A real-time inland flood inundation prediction method comprising: a flood arrival time calculation unit calculating the inundation depth distribution at a time step (w+1) by averaging multiple neighboring points from multiple plots that each show the relationship between the amount of inundation assumed for multiple different probability rainfall amounts and the flood arrival time at that time, which are recorded in the inundation depth distribution storage unit.

5. A real-time inland flood inundation prediction system according to claim 4, A real-time inland flood inundation prediction method in which the aforementioned multiple different probabilistic rainfall amounts include at least one of the probabilistic rainfall amounts for a specific year and the assumed maximum scale.

6. A real-time inland flood inundation prediction system according to claim 4, The aforementioned flood depth distribution storage section assumes the amount of inundation under different probability rainfall amounts, A forward-focused rainfall waveform in which rainfall is concentrated at the beginning of the rainy season, Intermediately concentrated rainfall waveforms, where rainfall is concentrated in the middle of the rainy period, A rainfall pattern characterized by a late-focused rainfall pattern where rainfall is concentrated towards the end of the rainy season, A real-time inland flood inundation prediction method comprising a model.

7. A real-time inland flood inundation prediction system according to any one of claims 1 to 6, further comprising: Multiple water level sensors are installed at predetermined locations within the prediction area, Equipped with, The aforementioned water level calculation unit is connected to each of the multiple water level sensors via a network. A real-time inland flood inundation prediction system comprising a water volume calculation unit configured to correct the water volume calculated in real time using the measured water level at the installation location observed by the water level sensor.

8. A real-time inland flood inundation prediction system according to claim 7, A real-time inland flood inundation prediction system comprising a water volume calculation unit configured to perform real-time calculations of the water volume using the measured water level at the installation location observed by the water level sensor as the initial water level.

9. A real-time inland flood inundation prediction system according to claim 7, A real-time inland flood inundation prediction system comprising a water volume calculation unit configured to complement the water level of the surrounding area with the water levels of adjacent locations among the water levels measured at multiple installation locations observed by the multiple water level sensors.

10. A real-time inland flood inundation prediction device that predicts inland flooding in a predicted area caused by rainfall in real time, under the conditions that the predicted area is divided into small watersheds, uniform rainfall occurs within each small watershed, and a pre-calculated inundation depth distribution occurs at each location within the predicted area for each small watershed, A data acquisition unit that acquires observed rainfall and predicted rainfall, A water level calculation unit calculates the current water level in real time based on the observed rainfall acquired by the data acquisition unit, the predicted rainfall, and the initial water level. A predicted inundation depth distribution model storage unit for storing multiple predicted inundation depth distribution models that have been pre-calculated for each inundation volume in the predicted area, A flood arrival time calculation unit for calculating the expected flood arrival time in the predicted area based on the rainfall intensity curve, Based on the flood volume calculated in real time by the flood volume calculation unit, a predicted flood depth distribution model extraction unit extracts a corresponding predicted flood depth distribution model from a plurality of predicted flood depth distribution models stored in the predicted flood depth distribution model storage unit. Equipped with, A real-time inland flood inundation prediction device comprising: a predicted inundation depth distribution model extraction unit which corrects the inundation volume calculated in real time by the inundation volume calculation unit to a corrected inundation volume considering the flood arrival time calculated by the flood arrival time calculation unit, and extracts a predicted inundation depth distribution model corresponding to the corrected inundation volume.

11. A real-time inland flood inundation prediction method that predicts inland flooding in a predicted area caused by rainfall in real time, under the conditions that the predicted area is divided into small watersheds, uniform rainfall occurs within each small watershed, and a pre-calculated inundation depth distribution occurs at each location within the predicted area for each small watershed, The process involves calculating the current water level in real time based on the initial water level, Based on the real-time calculated flood volume, the process involves extracting a corresponding predicted flood depth distribution model from a plurality of predicted flood depth distribution models that have been pre-calculated for each flood volume in the predicted area. Includes, In the process of extracting the corresponding predicted inundation depth distribution model, A real-time inland flood inundation prediction method comprising the steps of: calculating the expected flood arrival time when floods will reach a predicted area based on rainfall intensity using a flood arrival time calculation unit; correcting the real-time calculated inundation volume to a corrected inundation volume considering the calculated flood arrival time; and extracting a predicted inundation depth distribution model corresponding to the corrected inundation volume.

12. A real-time inland flood inundation prediction method according to claim 11, In the process of extracting the corresponding predicted inundation depth distribution model, The process involves preparing a flood depth distribution storage unit that stores the relationship between the amount of water inundated and the time of flood arrival as flood depth distribution data, The process involves preparing a rainfall intensity-specific water volume time change storage unit that stores rainfall intensity-specific water volume time change data calculated by flood analysis, which shows the relationship between water volume and elapsed time according to rainfall intensity, and A step of comparing the amount of water inundated at the current time, which is the amount of water inundated at the current time step (w), with the data on the time change of water inundated at different rainfall intensities stored in the storage unit for the time change of water inundated at different rainfall intensities, A step of calculating the time change in the amount of water inundated (ΔV(R)) due to an arbitrary amount of rainfall (R) that occurred from the amount of water inundated (V(t)) at the current time step (w) to the next time step (w+1), which is after a certain time period (Δw), from the water inundation amount (V(t)) at the current time step (w), using the water inundation amount time change data by rainfall intensity, A step of estimating the amount of water in the next time step (V(t+1)) based on the amount of change in water volume over time (ΔV(R)) of the water volume in the next time step (w+1) calculated above, A step of comparing the amount of water inundated (V(t+1)) at the aforementioned time step (w+1) with the inundation depth distribution data stored in the inundation depth distribution storage unit, A process of saving the rainfall intensity curve calculated from the rainfall waveform to the rainfall intensity curve storage unit, The process involves calculating the Kadoya-Fukushima formula using a quasi-linear storage-type function model calculation unit, The process involves calculating the point where the rainfall intensity curve stored in the rainfall intensity curve storage unit intersects with the quasi-linear storage function model, using the flood arrival time calculation unit as the flood arrival time. The flood arrival time calculated by the flood arrival time calculation unit is compared with the inundation depth distribution data stored in the inundation depth distribution storage unit, and the inundation volume (V) of the next time step (w+1) is calculated. A (T A The process of calculating )) and The flood depth distribution storage unit performs a step of calculating the averaged flood depth distribution for the time step (w+1) by averaging a plurality of neighboring points that have been recorded in advance. For the next time step (w+2) after a certain time interval (Δw) has elapsed from the previous time step (w+1), the process of comparing it with the time-varying data of inundation volume by rainfall intensity, and the process of determining the averaged inundation depth distribution for the previous time step (w+2) are repeated, thereby determining the averaged inundation depth distribution for each time interval. A real-time inland flood inundation prediction method, including...

13. A real-time inland flood inundation prediction method according to claim 12, In the process of preparing the flood depth distribution storage section, The process involves creating rainfall intensity formulas for multiple different probability rainfall amounts, For the rainfall intensity formulas for each probability rainfall amount created above, A forward-focused rainfall pattern where rainfall is concentrated at the beginning of the rainy season. Intermediately concentrated rainfall waveform, where rainfall is concentrated in the middle of the rainy period. A rainfall pattern characterized by a concentration of rainfall towards the end of the rainy season. The process involves performing flood analysis with the external force and outputting the flood analysis results at predetermined intervals, A real-time inland flood inundation prediction method, including...

14. A real-time inland flood inundation prediction method according to claim 12, A real-time inland flood inundation prediction method in which the aforementioned multiple different probabilistic rainfall amounts include at least one of the probabilistic rainfall amounts for a specific year and the assumed maximum scale.

15. A real-time inland flood inundation prediction method according to claim 13, In the process of preparing the flood depth distribution storage section, The process involves performing flood analysis on graphs representing periods of rising water levels, using rainfall forces as the external force when multiple different intensities of constant rainfall continue, and outputting the flood analysis results at predetermined intervals. The process involves performing flood analysis on graphs representing periods of decreasing water levels, using rainfall forces as the external force when multiple different intensities of constant rainfall continue, and outputting the flood analysis results at predetermined intervals. A real-time inland flood inundation prediction method, including...

16. A real-time inland flood inundation prediction method according to any one of claims 11 to 15, further comprising: The process involves obtaining the water level for each installation location, which is observed by multiple water level sensors installed at predetermined locations within the prediction area. The process involves correcting the real-time calculated water volume using the water level at the installation location observed by the water level sensor. A real-time inland flood inundation prediction method, including...

17. A real-time inland flood inundation prediction method according to claim 16, The process of correcting the amount of water in the reservoir is as follows: A real-time inland flood inundation prediction method, which includes the step of performing real-time calculation of the amount of inundation using the measured water level at the installation location observed by the water level sensor as the initial water level.

18. A real-time inland flood inundation prediction method according to claim 16, The process of correcting the amount of water in the reservoir is as follows: A real-time inland flood inundation prediction method, which includes the step of supplementing the water level of a surrounding area with the water level of adjacent measured water levels among the measured water levels at multiple installation locations observed by the multiple water level sensors.

19. A real-time inland flood inundation prediction program for predicting inland flooding in a predicted area caused by rainfall, under the conditions that the predicted area is divided into small watersheds, uniform rainfall occurs within each small watershed, and a pre-calculated inundation depth distribution occurs at each location within the predicted area for each small watershed, A function that calculates the current water level in real time based on the initial water level, Based on the real-time calculated flood volume, the function extracts a corresponding predicted flood depth distribution model from multiple predicted flood depth distribution models that have been pre-calculated for each flood volume in the predicted area. The system includes a function to calculate the expected flood arrival time in the predicted area based on rainfall intensity, correct the real-time calculated inundation volume to a corrected inundation volume considering the calculated flood arrival time, and extract a predicted inundation depth distribution model corresponding to the corrected inundation volume. A real-time inland flood inundation prediction program to enable computers to perform this task.

20. A computer-readable recording medium or storage device on which the program of claim 19 is recorded.

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