Flood prediction device, flood prediction method, and program

Abstract

The flood prediction device will perform water level prediction calculations appropriately. [Solution] The flood prediction device 170 includes a receiving unit 130 that receives rainfall prediction information, and an allocation resource determination unit 140 that determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall prediction information received by the receiving unit 130. The allocation resource determination unit 140 allocates resources to the process of performing water level prediction calculations on the condition that the average rainfall over a predetermined period exceeds a predetermined rainfall threshold.

Description

Technical Field

[0001] The present invention relates to a flooding prediction device, a flooding prediction method, and a program.

Background Art

[0002] Due to the influence of climate change, there is concern that flood damage is intensifying and the frequency is increasing. To reduce damage, it is considered effective to predict and announce the flood situation several hours ahead from the current time. Therefore, efforts have been made not only to predict river water levels from rainfall predictions but also to predict flooded areas and flood depths. For example, in the summary of Patent Document 1 below, "[Summary] Conventional flood prevention systems for flood disasters had low water level prediction accuracy several hours ahead during heavy rain, and it was difficult to share information in the region. Furthermore, if a large number of devices were to be installed, the device cost would be high, which was an obstacle to new introduction to disaster prevention agencies. [Solution] A disaster prevention system including an observation terminal device 1 with a water level sensor 2 and a camera 4 set, a cloud water level prediction unit 6 using a tank model, and a server 7. The adaptation to rainfall intensity and regionality can be achieved only by adjusting the parameters of the tank model, and by combining it with video information, the prediction accuracy and learning ability are improved. By separating the functions of water level observation, control by administrators, and information sharing and linking them on the cloud, compared with the conventional all-in-one type, the installation cost of a large number of observation terminals can be reduced, and the spread can be promoted." is described.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in the above-described technology, there is a desire to more appropriately execute the water level prediction calculation. This invention has been made in view of the circumstances described above, and aims to provide a flood prediction device, a flood prediction method, and a program that can appropriately perform water level prediction calculations. [Means for solving the problem]

[0005] To solve the above problems, the flood prediction device of the present invention is characterized by comprising a receiving unit that receives rainfall prediction information, and an allocation resource determination unit that determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall prediction information received by the receiving unit. [Effects of the Invention]

[0006] According to the present invention, water level prediction calculations can be performed appropriately. [Brief explanation of the drawing]

[0007] [Figure 1] This is a block diagram of the flood prediction system according to the first embodiment. [Figure 2] This is a sequence diagram of the overall operation of the flood prediction system. [Figure 3] This is a flowchart of the state determination processing routine in the first embodiment. [Figure 4] This is a flowchart of the flood inundation analysis routine in the first embodiment. [Figure 5] This is a sequence diagram of the distribution process in the flood prediction system. [Figure 6] This figure shows an example of a forecast information display window. [Figure 7] This figure shows an example of the management information window. [Figure 8] This is a flowchart of the state determination processing routine in the second embodiment. [Figure 9] This is a block diagram of a computer. [Modes for carrying out the invention]

[0008] [Summary of the Embodiment] When implementing a flood prediction system, high implementation and operating costs are a major challenge. Generally, a flood prediction system takes rainfall forecast information as input, performs flood inundation analysis, and outputs the results as a flood prediction. Flood inundation analysis is computationally intensive, and since the entire process must be completed within a limited time, high-performance computers are required. Purchasing such high-performance computers increases the implementation cost of the prediction system. In addition, because it is assumed that these computers will be operated in a predetermined manner for a predetermined period, it is difficult to respond quickly to changes in society and the environment.

[0009] To address this, renting high-performance computers from cloud services, etc., would reduce implementation costs and allow for quicker responses to changes. However, renting high-performance computers from cloud services, etc., would result in higher operating costs. The embodiments described below, in light of the circumstances mentioned above, aim to reduce the costs required for cloud services, etc., by adjusting the startup timing of processes in cloud services, etc., according to the situation, thereby reducing the operating costs of the flood prediction system. Various embodiments will be described below with reference to the attached drawings.

[0010] [First Embodiment] <Configuration of the first embodiment> Figure 1 is a block diagram of the flood prediction system 100 according to the first embodiment. In Figure 1, the flood prediction system 100 comprises a cloud service facility 170 (flood prediction device, computer), a weather information distribution server 180, and a user terminal 190. The cloud service facility 170 comprises a web server 110, a batch processing server 120, a storage server 130 (receiving unit, receiving means), a status determination server 140 (allocation resource determination unit, allocation resource determination means), and a prediction calculation server 150.

[0011] The configuration requirements for these cloud service facilities 170 may be configured using virtual machines, or they may be configured serverlessly by applying static web applications, storage services, or FaaS (Function as a Service).

[0012] The web server 110 sends a web page to the user terminal 190 in response to a request from the user terminal 190. The batch processing server 120 manages the prediction calculation server 150. Specifically, the batch processing server 120 creates the prediction calculation server 150 by provisioning in response to a request from the status determination server 140, and has the prediction calculation server 150 perform flood inundation analysis. Furthermore, when the flood inundation analysis on the prediction calculation server 150 is completed, the batch processing server 120 deletes the prediction calculation server 150 by deprovisioning.

[0013] The storage server 130 performs tasks such as loading and saving files. The status determination server 140 determines whether the area status, which is the state of the monitored area, is normal or in a warning state. The prediction calculation server 150 performs water level prediction calculations such as flood inundation analysis. The prediction calculation server 150 does not exist in the normal state, but exists in the warning state. When the area status changes from normal to warning, it is created (provisioned) by the batch processing server 120. Also, when the state changes from warning to normal and a predetermined time has elapsed, it is deleted (deprovisioned) by the batch processing server 120.

[0014] The weather information distribution server 180 is a server that distributes weather information. For example, the weather information distribution server 180 distributes rainfall forecast data (analyzed rainfall and short-term precipitation forecasts) made by the Japan Meteorological Agency. User terminal 190 accesses web server 110 based on user operations. User terminal 190 can be a PC (personal computer) or a smartphone. The user uses a web browser installed on user terminal 190 to view information obtained from web server 110.

[0015] Here, although a plurality of servers are prepared, the servers may be aggregated. That is, the web server 110, the batch processing server 120, the storage server 130, and the state determination server 140 are aggregated into one server. Thereby, a system including the web server 110, the prediction calculation server 150, the weather information distribution server 180, and the user terminal 190 may be used.

[0016] <Operation of the First Embodiment> Next, the operation of the first embodiment will be described. FIG. 2 is a sequence diagram of the overall operation in the flooding prediction system 100. In step S201 (reception process) of FIG. 2, the weather information distribution server 180 distributes weather information such as rainfall data to the storage server 130. For the distribution, a protocol such as SFTP PUSH may be applied. The distributed data is stored as a rainfall data file in the storage server 130. Since the weather information distribution server 180 continuously distributes the weather information at a predetermined time interval (for example, at 30-minute intervals), this step S201 is executed periodically (for example, once every 30 minutes).

[0017] When new weather information is distributed, the storage server 13 executes the process of step S202. Here, the storage server 130 notifies the state determination server 140 that new data has been distributed. When receiving the notification of step S202, the state determination server 140 executes the process of step S203. Here, first, the state determination server 140 determines whether the current area state is a normal state or a warning state, and stores the determination result in the storage server 130.

[0018] Then, the storage server 130 stores the history of the past area states. The details of the process for determining the area state will be described later. When the current area state is the normal state, the state determination server 140 prohibits the allocation of resources for the process of performing the water level prediction calculation, and waits for the next notification from the storage server 130.

[0019] On the other hand, if the current area status is in a warning state, in step S204 (allocation resource determination process), the status determination server 140 outputs a prediction calculation execution request to the batch processing server 120. When the batch processing server 120 receives the prediction calculation execution request, it checks whether the prediction calculation server 150 has been provisioned.

[0020] If the predictive computing server 150 is not provisioned, the batch processing server 120 performs provisioning of the predictive computing server 150 in step S205. On the other hand, if the predictive computing server 150 is already provisioned, the process in step S205 is omitted.

[0021] Next, when the process proceeds to step S206, the batch processing server 120 outputs a task execution command to the prediction calculation server 150. This task execution command instructs the prediction calculation server 150 to perform water level prediction calculations such as flood inundation analysis. Upon receiving the task execution command, the prediction calculation server 150 performs the water level prediction calculation in step S207 and saves the results of the water level prediction calculation to the storage server 130.

[0022] Next, in step S208, the batch processing server 120 performs deprovisioning of the prediction calculation server 150 if the following two conditions #1 and #2 are met. Condition #1: The last water level prediction calculation task executed on prediction calculation server 150 must be completed. Condition #2: The history of the area status within a specified time period in the past (e.g., within 1 hour) is all in a normal state. With the above conditions met, the series of processes shown in Figure 2 is completed.

[0023] The server with the heaviest processing load among the cloud service equipment 170 is the predictive computing server 150, while the processing load of the other servers is relatively small. Therefore, when a user rents the cloud service equipment 170, the longer the predictive computing server 150 is present, the higher the operating costs generally become. Consequently, if only the reduction of operating costs is considered, there is room to consider that it is preferable to immediately deprovision the predictive computing server 150 when condition #1 is met, regardless of condition #2 mentioned above.

[0024] However, provisioning in step S205 generally takes a certain amount of time. Therefore, if provisioning in step S205 is performed every time a prediction calculation execution request in step S204 occurs, there is a problem that the time lag until water level prediction calculation results such as flood inundation analysis are obtained will be large. Generally, when an area status becomes a warning state, it is highly likely that the warning state will persist for several hours. For this reason, in this embodiment, if a warning state has occurred even once within a predetermined time period in the past (for example, within 1 hour), the prediction calculation server 150 is kept running. This makes it possible to balance the reduction of operating costs with the reduction of time lag in this embodiment.

[0025] Figure 3 is a flowchart of the state determination processing routine in the first embodiment. Specifically, Figure 3 shows the details of the state determination process (step S203) in the state determination server 140 of the first embodiment. In step S301 of Figure 3, the status determination server 140 acquires rainfall data from the storage server 130. Here, it is preferable to apply the Japan Meteorological Agency's analyzed rainfall data to rainfall data from the past than the current time, and the Japan Meteorological Agency's short-term precipitation forecast to future rainfall data. The time interval for updating the rainfall data is, for example, 30 minutes, and the mesh size is, for example, 1 km. Analyzed rainfall is actual data up to the current time. Short-term precipitation forecast is predicted data up to 6 hours ahead from the current time, with a time resolution of 1 hour.

[0026] Next, in step S302, the state determination server 140 calculates the average precipitation R (average rainfall) for the target watershed over the previous X hours based on the acquired rainfall data. The precipitation over the previous X hours is the sum of the rainfall over the X hours prior to that time. Here, "X" is a numerical value, and for example, "3", "6", "12", "24", and "48" are often applied. As for the numerical value X, it is preferable to apply "3" or "6" if the watershed is small and steep, and "24" or "48" if the watershed is large and flat. In the processing of step S302, the average precipitation R for the target watershed over the previous X hours is obtained in six different values ​​from the current time, at one-hour intervals up to six hours ahead.

[0027] Next, when the process proceeds to step S303, the state determination server 140 determines whether at least one of the six types of basin-averaged previous X-hour precipitation R exceeds a predetermined precipitation threshold RT. If the determination result is positive, the state determination server 140 sets the area state to alert state; if the determination result is negative, it sets the area state to normal state. Furthermore, the state determination server 140 saves the current area state as a state data file to the storage server 130. With this, the state determination process is completed.

[0028] Figure 4 is a flowchart of the flood inundation analysis routine in the first embodiment. Specifically, Figure 4 shows the details of the flood inundation analysis (step S207) in the prediction calculation server 150. In Figure 4, when the process proceeds to step S401, the prediction calculation server 150 repeats the processes from step S402 to step S408 until the time t in the model reaches the end time te of the prediction period.

[0029] First, in step S402, the prediction calculation server 150 performs runoff analysis by applying a distributed runoff model. Next, in step S403, the prediction calculation server 150 performs one-dimensional unsteady flow analysis by applying a river channel model. Next, in step S404, the prediction calculation server 150 performs two-dimensional unsteady flow analysis by applying a floodplain model. Next, in step S405, the prediction calculation server 150 performs water exchange between the distributed runoff model and the river channel model.

[0030] Next, in step S406, the prediction calculation server 150 exchanges water between the river channel model and the floodplain model. Then, in step S407, the prediction calculation server 150 saves the analysis results from steps S402 to S406 as an inundation area / inundation depth data file to the storage server 130. The inundation area / inundation depth data file is, for example, a file that records time-series data up to 6 hours ahead with a mesh size of 25m and intervals of 5 minutes. In step S408, the prediction calculation server 150 increases the time t in the model by the time step width dt.

[0031] Figure 5 is a sequence diagram of the distribution process in the flood prediction system 100. In step S501, the user terminal 190 retrieves a web page from the web server 110. This web page contains instructions to retrieve rainfall data files, status data files, and flood area / flood depth data files stored in the storage server 130. The user terminal 190 has a web browser installed, and the retrieved web page is displayed by the web browser.

[0032] Next, in step S502, the user terminal 190 acquires the rainfall data file. Next, in step S503, the user terminal 190 acquires the status data file. Next, in step S504, the user terminal 190 acquires the flooded area / flood depth data file. However, the order of steps S502 to S504 may be changed, and these steps are executed asynchronously. In addition, each data file may be acquired automatically or based on user operations on the web browser. Next, in step S505, the user terminal 190 displays a screen based on the contents of the acquired data file. With this, the distribution processing sequence is completed.

[0033] Figure 6 shows an example of a prediction information display window 600. This prediction information display window 600 is displayed on the user terminal 190 in step S505 of the distribution processing sequence (Figure 5). The forecast information display window 600 includes a menu button 601, a status display unit 610, a rainfall display unit 620, and a flood area / flood depth display unit 630.

[0034] When the user performs a predetermined operation on the menu button 601, the user terminal 190 displays the management information window 700 (see Figure 7), which will be described later, instead of the prediction information display window 600. In addition, the status display unit 610 displays either "Alert" indicating an alert state or "Normal" indicating a normal state as the current area status.

[0035] The rainfall display unit 620 includes a cursor line 621, a bar graph 622, and a line graph 624. The cursor line 621 indicates the current time. The bar graph 622 and line graph 624 display the basin-average rainfall R over the past X hours, as specified by the user. In the illustrated example, the bar graph 622 shows the basin-average rainfall over the past hour, and the line graph 624 shows the basin-average rainfall over the past six hours.

[0036] The flooded area / flood depth display unit 630 includes a slider bar 631 and a map display unit 633. The map display unit 633 displays the predicted flood depth and flooded area on a map. More specifically, if the area status is in a warning state and flooding is expected, the flooded area / flood depth display unit 630 displays the predicted flood depth and flooded area up to 6 hours in advance from a specified reference time. The slider bar 631 is used to specify the aforementioned reference time.

[0037] The inundation depth in the map display unit 633 is shown in 25m mesh, and colors corresponding to the inundation depth are superimposed on the map. By default, the map display unit 633 displays the maximum inundation depth for a time range of up to 6 hours from the reference time. However, the user can select a time series of inundation depth for a specific time by performing a predetermined operation. Also, for example, if the area condition is normal and inundation is not expected, the inundation area / inundation depth display unit 630 will display the message, "No inundation is currently expected."

[0038] Figure 7 shows an example of the management information window 700. This management information window 700 is displayed on the user terminal 190 when the user performs a predetermined operation on the menu button 601 of the prediction information display window 600 (see Figure 6). The management information window 700 includes a menu button 701 and a server information section 710. When a user performs a predetermined operation on the menu button 701, the user terminal 190 displays the prediction information display window 600 (see Figure 6) instead of the management information window 700.

[0039] The server information unit 710 displays whether the flood prediction system 100 is currently normal or abnormal, and whether each server 110-150 is normal or abnormal. The server information unit 710 also displays whether the prediction calculation server 150 is currently idle (deprovisioned), running (provisioning), on standby (provisioned, no tasks), or running (provisioned, tasks). Furthermore, the server information unit 710 displays the operational history of the prediction calculation server 150 up to the present.

[0040] [Second Embodiment] Next, a flood prediction system according to the second embodiment will be described. In the description of each embodiment, parts corresponding to parts of the other embodiments described above will be denoted by the same reference numerals, and their descriptions may be omitted. In the first embodiment described above, the state determination processing routine shown in Figure 3 was applied as the state determination processing (step S203 in Figure 2) in the state determination server 140. In the second embodiment, the state determination processing routine shown in Figure 8 is applied instead of the processing in Figure 3. Except for the points mentioned above, the configuration and operation of the computer system in the second embodiment are the same as those in the first embodiment (Figures 1, 2, 4 to 7).

[0041] Figure 8 is a flowchart of the state determination processing routine in the second embodiment. Specifically, Figure 8 shows the details of the state determination process (step S203) in the state determination server 140 of the second embodiment. In step S801 of Figure 8, the status determination server 140 acquires rainfall data from the storage server 130. The contents of the rainfall data are the same as those described in the first embodiment.

[0042] Next, in step S802, the state determination server 140 predicts the river water level L for each predetermined river cross-section. This process is preferably implemented by a combined model of runoff analysis using a distributed runoff model and one-dimensional unsteady flow analysis using a river channel model. Next, when the process proceeds to step S803, the state determination server 140 determines whether at least one of the river water levels L exceeds the corresponding river water level threshold LT. This river water level threshold LT is, for example, the height of the levee crest, but it may also be the planned high water level. If the determination result is positive, the state determination server 140 sets the area state to the alert state, and if the determination result is negative, it sets the area state to the normal state. Furthermore, the state determination server 140 saves the current area state as a state data file to the storage server 130. With this, the state determination process is completed.

[0043] In Figure 3 of the first embodiment described above, the area condition was determined based on rainfall data. While predictions based on rainfall data are computationally inexpensive, depending on the topography of the target area, it may be difficult to accurately determine the area condition based on rainfall data. For example, depending on the topography of the target area, the area condition may be determined to be in a warning state even if the risk of flooding is small. Conversely, the area condition may be determined to be in a normal state even if the risk of flooding is large.

[0044] Therefore, in this embodiment, the river water level L within or near the target area is predicted to determine the area's condition. This improves the accuracy of predicting whether or not flooding will occur compared to the first embodiment, and allows for a more accurate determination of the area's condition.

[0045] [Computer Configuration] Figure 9 is a block diagram of the Computer 980. The cloud service equipment 170, weather information distribution server 180, and user terminal 190 shown in Figure 1 are all equipped with one or more computers 980 as shown in Figure 9. In Figure 9, the computer 980 comprises a CPU 981, a memory unit 982, a communication port 983, an input / output port 984, and a media port 985. Here, the memory unit 982 comprises a RAM 982a, a ROM 982b, and an SSD (Solid State Drive) 982c.

[0046] The communication port 983 is connected to the communication circuit 986. The input / output port 984 is connected to the input / output device 987. The media port 985 reads and writes data to the recording medium 988. The ROM 982b stores the IPL (Initial Program Loader) and other programs executed by the CPU. The SSD 982c stores application programs and various data. The CPU 981 implements various functions by executing application programs and other data read from the SSD 982c into the RAM 982a.

[0047] [Differentiation] The present invention is not limited to the embodiments described above, and various modifications are possible. The embodiments described above are illustrative examples provided to facilitate understanding of the present invention, and are not necessarily limited to those comprising all the described configurations. Furthermore, it is possible to replace parts of the configuration of one embodiment with those of another embodiment, and to add configurations from other embodiments to the configuration of one embodiment. It is also possible to delete parts of the configuration of each embodiment, add other configurations, or replace them with other configurations. In addition, the control lines and information lines shown in the figures are those considered necessary for explanation, and do not necessarily represent all control lines and information lines required in the product. In practice, it can be assumed that almost all configurations are interconnected. Possible modifications to the above embodiments are as follows, for example.

[0048] (1) In steps S205 and S208 of each of the above embodiments (see Figure 2), the batch processing server 120 performed provisioning and deprovisioning of the predictive computing server 150. This is based on the premise that the operating cost of the predictive computing server 150 is determined according to the existence time of the predictive computing server 150 of the cloud service facility 170. However, the operating cost of the predictive computing server 150 may also be determined by the resources allocated to the predictive computing server 150 (for example, the number of time slots for time-sharing processing, the allocated capacity of RAM 982a shown in Figure 9, the allocated capacity of SSD 982c, etc.).

[0049] In such cases, the prediction calculation server 150 may be kept in a constant state, and in step S205, resources for water level prediction calculations may be allocated, and in step S208, those resources may be released. In the first and second embodiments described above, provisioning and deprovisioning the prediction calculation server 150 can be considered equivalent to allocating or releasing resources for water level prediction calculations. Therefore, this modified example and the first and second embodiments described above have in common that resources for water level prediction calculations are allocated or released. In addition, the state determination server 140 will have a function to determine the resources to be allocated to the process of performing water level prediction calculations.

[0050] Since the hardware of the cloud service equipment 170 and user terminal 190 in the above embodiment can be implemented using a general-purpose computer, the programs that execute the processes corresponding to each block diagram, flowchart, and sequence diagram described above, as well as other various processes described above, may be stored in a storage medium (a computer-readable recording medium on which the program is recorded) or distributed via a transmission line.

[0051] (2) Although the processes corresponding to each block diagram, flowchart, and sequence diagram described above, and other various processes described above, were explained as software processes using a program in the above embodiment, some or all of them may be replaced with hardware processes using an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), etc.

[0052] [Effects of the Embodiment] As described above, according to each embodiment, the flood prediction device (170) includes a receiving unit (130) that receives rainfall prediction information, and an allocation resource determination unit (140) that determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall prediction information received by the receiving unit (130). This makes it possible to determine the resources to be allocated to the process of performing water level prediction calculations when necessary, so that the water level prediction calculations can be performed appropriately.

[0053] Furthermore, it is even more preferable that the resource allocation determination unit (140) allocates resources to the process of performing water level prediction calculations on the condition that the average precipitation (R) over a predetermined period exceeds a predetermined precipitation threshold RT.

[0054] Furthermore, it is even more preferable that the resource allocation determination unit (140) prohibits the allocation of resources to the water level prediction calculation process when the average precipitation (R) over a predetermined period is less than or equal to a predetermined precipitation threshold RT. By applying the average precipitation (R) in this way, it is possible to prevent resource allocation from being repeated at a high frequency.

[0055] Furthermore, it is even more preferable that the resource allocation determination unit (140) calculates the average precipitation (R) per predetermined time interval based on the rainfall forecast information received by the receiving unit (130). This allows the predetermined time interval to be used as the unit for resource allocation intervals.

[0056] Furthermore, as in the second embodiment, it is even more preferable that the resource allocation determination unit (140) includes a function to calculate the river water level L based on rainfall forecast information, and a function to allocate resources to a process that performs water level forecast calculations, provided that the river water level L during a predetermined period exceeds a predetermined river water level threshold LT. This makes it possible to determine the timing of resource allocation based on the river water level L with even greater accuracy. [Explanation of Symbols]

[0057] 100 Flood Prediction Systems 130 Storage server (receiving unit, receiving means) 140 Status determination server (allocation resource determination unit, allocation resource determination means) 170 Cloud service equipment (flood prediction device, computer) 600 Prediction Information Display Window 700 Management Information Window 980 Computer L River water level R Basin average previous X hour precipitation (average precipitation) LT River Water Level Threshold RT precipitation threshold S201 Step (Reception Process) S204 Step (Resource Allocation Determination Process)

Claims

1. A receiving unit that receives rainfall forecast information, The system includes an allocation resource determination unit that determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall prediction information received by the receiving unit. A flood prediction device characterized by the following features.

2. The aforementioned resource allocation determination unit, Resources are allocated to the process of performing the water level prediction calculation, on the condition that the average precipitation over a predetermined period exceeds a predetermined precipitation threshold. The flood prediction device according to claim 1, characterized in that it is a flood prediction device.

3. The aforementioned resource allocation determination unit, If the average precipitation over a predetermined period is below a predetermined precipitation threshold, the allocation of resources to the process that performs the water level prediction calculation is prohibited. The flood prediction device according to claim 1, characterized in that it is a flood prediction device.

4. The resource allocation determination unit calculates the average precipitation per predetermined time interval based on the rainfall forecast information received by the receiving unit. The flood prediction device according to claim 2.

5. The aforementioned resource allocation determination unit, Based on the aforementioned rainfall forecast information, the function calculates the river water level, The system includes a function to allocate resources to the process of performing the water level prediction calculation, on the condition that the river water level during a predetermined period exceeds a predetermined river water level threshold. The flood prediction device according to claim 1, characterized in that it is a flood prediction device.

6. The receiving process for receiving rainfall forecast information, The computer is instructed to perform a resource allocation determination process, which determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall forecast information received in the aforementioned receiving process. A flood prediction method characterized by the following features.

7. Computers, Receiving means for receiving rainfall forecast information, A resource allocation determination means determines the resources to be allocated to the process of performing water level prediction calculations based on the rainfall forecast information received by the receiving means. A program designed to function as such.

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