Mountain flood inundation risk grade classification method, device and electronic equipment

Abstract

The present application relates to the technical field of mountain torrent disaster prevention research, and discloses a mountain torrent submergence risk grade classification method, device and electronic equipment, the method comprising: obtaining digital elevation model data of a target area, and generating design rainfall data of different return periods for sub-basins; based on the digital elevation model data and the design rainfall data, a two-dimensional hydrodynamic model is constructed, mountain torrent submergence simulation is performed, and mountain torrent submergence water depth distribution results of each return period are obtained; from the submergence water depth distribution results of each return period, rasterized disaster submergence results meeting a preset disaster water depth threshold are extracted, and then disaster submergence range vectors are obtained; based on a preset mapping relationship between return period intervals and risk grades, the disaster submergence range vectors of different return periods are subjected to spatial superposition analysis and mapped into different mountain torrent submergence risk grades, the present application is based on physical mechanism simulation and quantitative threshold judgment, and a complete technical process from rainfall input to mountain torrent submergence risk grade output is established.

Description

Technical Field

[0001] This invention relates to the field of research technology on flash flood disaster prevention and control, specifically to methods, devices, and electronic equipment for classifying flash flood inundation hazard levels. Background Technology

[0002] Flash floods are sudden and destructive, making them one of the most common natural disasters in mountainous areas. Scientifically and accurately delineating flash flood hazard zones is the scientific basis for risk prevention and control, implementing early warning and evacuation measures, and developing emergency response plans. However, many technical challenges remain in classifying flash flood hazard levels and quantifying flash flood risk grades.

[0003] Currently, methods for classifying the risk level of flash flood disasters mainly fall into two categories: one is the common empirical index method based on historical disaster statistics and geomorphological units. This method relies on historical data and empirical judgment, and its applicability is poor in areas without historical flash flood disaster data or in areas where the underlying surface has changed significantly. The other is a comprehensive qualitative evaluation based on indicators such as rainfall risk in mountainous areas, building vulnerability, and the vulnerability of disaster-bearing bodies. For example, the existing technology (CN115456463 ​​A) discloses a method and system for classifying the risk level of flash flood disaster hazard zones. This technology collects basic information of the hazard zone (such as boundaries, land use, and river networks) and information on protected objects (such as population, houses, and flood control capacity), establishes a comprehensive evaluation index system with "hazard - disaster-bearing body - vulnerability" as the three major risk items, and uses subjective weighting methods such as the analytic hierarchy process (AHP) to assign weights to each risk item, evaluation index, and sub-indicator. Based on the calculated risk level parameters, a threshold is set to classify the previously divided hazard zone units (usually based on towns, villages, or watersheds) into "high, medium, and low" risk levels.

[0004] Existing technologies are essentially comprehensive evaluation methods based on historical survey data, statistical characteristics, and expert experience. They can only be used to evaluate the overall area of ​​a pre-defined complete area, such as a watershed, town, or village, through hierarchical analysis. They cannot consider the heterogeneity of topographic spatial distribution within the area, nor can they quantify the threat of flash floods to specific areas or locations. They cannot answer the core disaster-causing question of "how deep and how large the flash flood is," resulting in a lack of direct physical data support for hazard level classification. In the context of rapid economic development in mountainous areas and the normalization of extreme weather, they are unable to meet the operational needs of flash flood prevention moving towards "dynamic assessment and precise prevention." Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for classifying flash flood inundation hazard levels, in order to solve the problem that existing methods for classifying flash flood hazard levels rely on historical statistics and experience-based judgments, and cannot quantitatively describe the spatial distribution of inundation depth and the spatiotemporal boundaries of the disaster-causing range during the evolution of flash floods.

[0006] In a first aspect, the present invention provides a method for classifying the hazard level of flash floods, the method comprising: Acquire digital elevation model data for the target area and generate design rainfall data with different return periods for the sub-basins of the target area; Based on digital elevation model data and design rainfall data, a two-dimensional hydrodynamic model is constructed to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period. From the inundation depth distribution results of each return period, the rasterized disaster-causing inundation results that meet the preset disaster-causing depth threshold are extracted and vectorized to obtain the disaster-causing inundation range vector for each return period; Based on the pre-defined mapping relationship between the return period interval and the hazard level, the vector of the disaster-causing inundation range with different return periods is spatially superimposed and analyzed to map it into different flash flood inundation hazard levels.

[0007] This invention provides a method for classifying flash flood inundation hazard levels. It constructs a two-dimensional hydrodynamic model to physically simulate flash flood inundation processes under different return periods of designed rainfall scenarios, obtaining the inundation depth distribution results for each spatial unit of the entire watershed under each return period. Based on a preset disaster-causing depth threshold, it extracts and vectorizes the disaster-causing inundation range for each return period, establishing a quantitative mapping relationship between the occurrence probability of the rainstorm return period and the hazard level. Through spatial overlay analysis, it achieves the classification of flash flood inundation hazard levels. Based on physical mechanism simulation and quantitative threshold judgment, this invention establishes a complete technical process from rainfall input to hazard level output, giving the hazard level classification results clear physical meaning and reproducibility. This provides scientific and objective decision support for flash flood disaster prevention and control, solving the problem that existing flash flood hazard level classification methods rely on historical statistics and experience-based judgments, failing to quantitatively describe the spatial distribution of inundation depth and the spatiotemporal boundaries of the disaster-causing range during flash flood evolution.

[0008] In one optional implementation, acquiring digital elevation model data of the target area includes: The digital elevation model data is preprocessed for hydrological analysis and then converted into raster data, which is the standard input for the model.

[0009] In the above technical solution, by performing hydrological analysis preprocessing on the digital elevation model data, defects such as depressions in the original topographic data are eliminated, and standardized input data that meets the calculation requirements of the hydrodynamic model is generated. This provides an accurate and continuous topographic basis for subsequent flash flood simulation, ensuring the physical consistency between the physical simulation process and the actual water flow path, thereby improving the reliability of the spatial distribution results of flash flood inundation water depth under each return period.

[0010] In one alternative implementation, design rainfall data with different return periods are generated for sub-basins of the target area, including: Obtain the hydrological statistical parameters of the sub-basins of the target area, and calculate the design total rainfall for different return periods based on the hydrological statistical parameters and a probability distribution model. Based on the preset rainfall time-history allocation standard, the total design rainfall with different return periods is allocated to each time period according to the weight, generating design rainfall data that changes over time.

[0011] In the aforementioned technical solution, hydrological statistical parameters for data-deficient areas are obtained by consulting hydrological handbooks and atlases. A probability distribution model is used to deduce the total design rainfall for different return periods, and rainfall weights for each time period are generated by combining this with a preset time-history allocation standard. This achieves a standardized conversion from statistical parameters to time-series rainfall data. This method provides a standardized approach for generating design rainfall inputs for flash flood gullies with no or scarce data, giving rainfall inputs at different return periods clear frequency significance and physical basis. It provides driving data consistent with regional hydrological characteristics for subsequent flash flood inundation simulations.

[0012] In one optional implementation, a two-dimensional hydrodynamic model is constructed based on digital elevation model data and design rainfall data to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period, including: The preprocessed raster data is converted into raster cells, and each raster cell is assigned an elevation value corresponding to its location. Based on the designed rainfall data, the runoff generation process data of each grid cell during the entire rainfall process are calculated through a hydrological model; Based on grid cells containing elevation values ​​and runoff process data, a computable two-dimensional hydrodynamic model with real terrain and runoff information is generated for the target area. A two-dimensional hydrodynamic model was used to simulate flash flood inundation, and the distribution of flash flood inundation depth at each return period was obtained.

[0013] In the aforementioned technical solution, the preprocessed digital elevation model data is discretized into grid cells with real elevation information. Based on the designed rainfall data, the runoff generation process of each grid cell is calculated using a hydrological model, thereby generating a two-dimensional hydrodynamic model containing both topographic and runoff information. This allows the model to simultaneously reflect topographic features and runoff input in each spatial cell. This method realizes a physical process-driven approach from rainfall input to inundation simulation, enabling the distribution of flash flood inundation depth at various return periods to quantitatively reflect the comprehensive impact of topographic spatial heterogeneity and the spatiotemporal variation of runoff generation on the evolution of flash floods. This provides physically based quantitative data for subsequent disaster extent extraction.

[0014] In one optional implementation, a two-dimensional hydrodynamic model is used to simulate flash flood inundation, obtaining the flash flood inundation depth distribution results for each return period, including: The water depth and flow velocity of each grid cell at the start of the simulation are set to preset values, and the downstream boundary conditions for free outflow are set according to the outlet topography of each sub-basin. The runoff generation process data for each return period are input into a two-dimensional hydrodynamic model time by time to simulate the evolution of floods in the corresponding sub-basins and obtain the simulation results for each return period. From the simulation results of each return period, the maximum water depth value of each grid cell during the entire simulation period is extracted to generate the flash flood inundation water depth distribution results for the corresponding return period.

[0015] In the aforementioned technical solution, by setting initial water depth and flow velocity to zero and allowing free outflow downstream, the simulation of flash flood evolution is provided with a physically realistic initiation state and outflow conditions. The runoff generation process data for each return period are input into the two-dimensional hydrodynamic model time-by-time, enabling the simulation to dynamically recreate the spatiotemporal evolution of the flood in the watershed. Furthermore, the maximum water depth value of each grid cell during the entire rainfall process is extracted from the complete simulation results for each return period, generating the flash flood inundation depth distribution results for each return period. This method achieves hydrodynamic simulation of the entire process from rainfall input to flood evolution, ensuring that the acquired inundation depth data reflects the true and dynamic spatial distribution characteristics of flash floods under complex terrain conditions.

[0016] In one optional implementation, from the inundation depth distribution results of each return period, rasterized disaster-causing inundation results that meet a preset disaster-causing depth threshold are extracted, and vectorized to obtain the disaster-causing inundation range vector for each return period, including: From the flash flood inundation simulation results of each return period, the maximum water depth value of each grid cell during the entire simulation period is extracted to generate the maximum inundation water depth distribution map for the corresponding return period; Based on the preset disaster-causing water depth threshold, grid cells with water depth values ​​greater than or equal to the corresponding preset disaster-causing water depth threshold in the maximum inundation water depth distribution map are identified as disaster-causing grids, and gridded disaster-causing inundation results for each return period are extracted and generated. The rasterized inundation results for each return period are converted into vector surface features, generating inundation range vectors for each return period.

[0017] In the above technical solution, the maximum water depth value of each grid cell within the entire simulation period is extracted from the flash flood inundation simulation results of each return period, generating a maximum inundation water depth distribution map for each return period. Based on a preset disaster-causing water depth threshold, grid cells with water depth values ​​greater than or equal to the threshold are identified as disaster-causing grids, and rasterized disaster-causing inundation results for each return period are extracted and generated. Then, the raster results are converted into vector surface features to form a disaster-causing inundation range vector for each return period. This method converts continuous water depth data generated by physical simulation into disaster-causing ranges with clear judgment criteria, ensuring that the extracted inundation range directly corresponds to the disaster-causing conditions of the disaster-bearing body, providing fundamental data with physical meaning and spatial accuracy for subsequent hazard level classification.

[0018] In one optional implementation, based on a preset mapping relationship between return period intervals and hazard levels, the disaster-causing inundation range vectors with different return periods are spatially superimposed and analyzed to map them into different flash flood inundation hazard levels, including: The disaster-causing inundation range vectors for each return period are sorted from smallest to largest according to the return period; Based on the pre-defined mapping relationship between the return period interval and the hazard level, starting from the disaster-causing inundation range with the lowest return period, the disaster-causing inundation range with the highest return period is subtracted from the disaster-causing inundation range with the lowest return period to obtain the independent area for each hazard level. The basic geographic information of the target area is obtained, the independent areas of each hazard level are merged, and the data is spatially overlaid with the basic geographic information to form a distribution map of flash flood hazard levels.

[0019] In the aforementioned technical solution, the disaster-causing inundation range vectors for each return period are sorted from smallest to largest according to the return period. Based on the preset mapping relationship between return period intervals and hazard levels, starting from the lowest return period, the inundation ranges of all low return periods are subtracted from the inundation ranges of high return periods to obtain independent regions corresponding to each hazard level. Then, basic geographic information of the target area is obtained, and the independent regions are merged and spatially overlaid to generate a flash flood inundation hazard level distribution map. This method, through a layer-by-layer pruning spatial analysis, maps different frequencies of rainstorm return periods to hazard level regions with clear spatial boundaries, ensuring that each level of region is spatially mutually exclusive and logically self-consistent. The hazard level classification results not only reflect the disaster-causing range of rainstorms of different frequencies but can also be directly applied to disaster prevention decision-making in conjunction with basic geographic elements.

[0020] In one optional implementation, the independent areas for each hazard level are merged and spatially overlaid with basic geographic information to form a flash flood hazard level distribution map, including: The disaster-causing inundation area with a recurrence period less than or equal to the first preset threshold is classified as an extremely high-risk area; the disaster-causing inundation area with a recurrence period greater than the first preset threshold and less than or equal to the second preset threshold is classified as a high-risk area; the disaster-causing inundation area with a recurrence period greater than the second preset threshold and less than or equal to the third preset threshold is classified as a medium-risk area; and the disaster-causing inundation area with a recurrence period greater than the third preset threshold and less than or equal to the fourth preset threshold is classified as a low-risk area. By spatially overlaying the delineated independent areas of different hazard levels with basic geographic information, a distribution map of flash flood inundation hazard levels is generated.

[0021] In the above technical solution, a quantitative correlation is established between the recurrence period of rainstorms at different frequencies and the hazard level by setting a preset threshold, so that each level area has a clear frequency meaning and spatial boundary. The results after superimposing basic geographic information can directly reflect the spatial relationship between the dangerous area and geographic elements such as villages and roads, providing a spatial and visual decision-making basis for graded management and emergency avoidance.

[0022] Secondly, the present invention provides a device for classifying the risk level of flash floods, the device comprising: The rainfall calculation module is used to acquire digital elevation model data of the target area and generate design rainfall data with different return periods for the sub-basins of the target area; The hydrodynamic simulation module is used to construct a two-dimensional hydrodynamic model based on digital elevation model data and design rainfall data to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period. The disaster extraction module is used to extract rasterized disaster inundation results that meet the preset disaster inundation depth threshold from the inundation depth distribution results of each return period, and vectorize them to obtain the disaster inundation range vector of each return period. The spatial analysis and classification module is used to perform spatial overlay analysis on the disaster-causing inundation range vectors with different return periods based on the preset mapping relationship between return period intervals and hazard levels, and map them into different flash flood inundation hazard levels.

[0023] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the flash flood hazard level classification method of the first aspect or any corresponding embodiment described above.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the flash flood hazard level classification method of the first aspect or any corresponding embodiment described above.

[0025] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the method for classifying flash flood inundation hazard levels according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first method for classifying the risk level of flash floods according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the method for classifying the risk level of flash floods according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the local standard design for the rainstorm time-history allocation process in City A according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a flash flood inundation hazard level classification device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0030] As an optional application scenario of this invention, such as Figure 1As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0031] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0032] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0033] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0034] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0035] According to an embodiment of the present invention, a method for classifying the risk level of flash flood inundation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] This embodiment provides a method for classifying flash flood inundation hazard levels, which can be used in the aforementioned electronic devices or terminal devices. Figure 2 This is a flowchart of a method for classifying flash flood inundation hazard levels according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain digital elevation model data for the target area and generate design rainfall data with different return periods for the sub-basins of the target area.

[0037] The target area refers to the specific geographical space where flash flood inundation simulation and hazard level delineation are required. It is usually a sub-basin or flash flood gully as a unit, with clear hydrological boundaries and topographic features.

[0038] Different return periods refer to design rainstorm scenarios with different probabilities of occurrence (or an average occurrence every so many years) derived from hydrological frequency analysis, used to simulate the extent of flash floods that may be triggered by rainfall of different magnitudes.

[0039] The designed rainfall data is spatially distributed data generated based on hydrological frequency analysis and rainstorm time history allocation at a specific return period. It describes the process of total rainfall changing over time and is used as input for flash flood inundation simulation.

[0040] Digital Elevation Model (DEM) data of the target area is digital topographic data stored in raster form that reflects the spatial distribution of surface elevation in the target area. It is used to quantitatively describe the surface undulation and serves as the basic input for constructing a two-dimensional hydrodynamic model.

[0041] Specifically, a series of hydrological analyses are performed on the DEM data, including filling depressions, calculating flow direction, and dividing the data into sub-basins. The data is then converted into a standard input format for the model (e.g., .asc file). A set of design rainstorm processes with different return periods (e.g., 5, 10, 20, and 100-year return periods) is generated for each sub-basin, resulting in design rainfall data for different return periods for the sub-basins of the target area.

[0042] Step S202: Based on the digital elevation model data and the design rainfall data, a two-dimensional hydrodynamic model is constructed to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period.

[0043] Among them, flash flooding refers to the natural process in which short-duration heavy rainfall in mountainous or hilly areas causes surface runoff to accumulate rapidly, river levels to rise sharply, and floods to overflow the channel and spread along the terrain, inundating surrounding land and facilities.

[0044] The two-dimensional hydrodynamic model is a numerical model based on two-dimensional shallow water equations used to simulate the spatiotemporal evolution of floods on complex surfaces. It can dynamically output the spatial distribution results of hydraulic elements such as inundation depth and flow velocity using grid cells as the calculation unit.

[0045] The results of flash flood inundation depth distribution refer to the spatial distribution data of the maximum water depth value of each spatial unit in the target area under the design rainfall conditions of each return period, obtained by simulation through a two-dimensional hydrodynamic model. It is used to quantitatively describe the inundation range and degree of flash floods caused by rainstorms of different frequencies.

[0046] Specifically, firstly, runoff generation is calculated using a hydrological model, and a hydrological model is constructed and calibrated. Considering the most unfavorable factors, the grid-distributed runoff generation results of the watershed under saturated soil moisture conditions are calculated. Then, a two-dimensional hydrodynamic model is constructed. Using the grid runoff generation results as input, initial conditions and boundary conditions are set, and multi-scenario simulations are performed to calculate the maximum inundation depth of each grid cell in each return period throughout the entire simulation process.

[0047] Step S203: Extract the rasterized disaster-causing inundation results that meet the preset disaster-causing water depth threshold from the inundation depth distribution results of each return period, and vectorize them to obtain the disaster-causing inundation range vector for each return period.

[0048] Among them, the rasterized disaster-causing inundation result refers to the binary raster data extracted from the maximum inundation depth distribution map of each return period based on the preset disaster-causing water depth threshold, and the grid cells with water depth values ​​greater than or equal to the threshold are identified as disaster-causing cells. It is used to quantitatively describe the spatial location and range that meet the disaster-causing conditions.

[0049] The disaster-causing inundation range vector for each return period refers to the disaster-causing range boundary data stored in vector format after converting the rasterized disaster-causing inundation results for each return period into vector surface features. It is used for subsequent spatial overlay analysis and hazard level classification.

[0050] Specifically, based on the disaster-causing water depth threshold (e.g., 0.2 meters) for typical disaster-bearing bodies (such as people and vehicles) affected by flash floods, the inundation results for all time periods are processed in batches to extract the rasterized disaster-causing inundation results for each rainfall scenario. Spatial analysis of disaster-causing inundation range: The disaster-causing inundation range raster under each scenario is converted into vector polygons. Based on high-definition remote sensing images and according to the actual distribution of mountain terrain, the results are processed into smooth surface vector files.

[0051] Step S204: Based on the preset mapping relationship between the return period interval and the hazard level, the disaster-causing inundation range vectors with different return periods are spatially superimposed and analyzed to map them into different flash flood inundation hazard levels.

[0052] Specifically, the classification of flash flood inundation hazard zones establishes a direct mapping rule between "rainstorm recurrence period" and "hazard level," converting inundation areas with different recurrence periods into flash flood inundation hazard levels. Inundation areas with a recurrence period less than or equal to the first threshold (e.g., once every 5 years) are designated as extremely high-risk zones; inundation areas with a recurrence period greater than the first threshold but less than or equal to the second threshold (e.g., once every 5 to 10 years) are designated as high-risk zones; and so on. Spatial overlay analysis is used to classify extremely high-risk zones, high-risk zones, medium-risk zones, and low-risk zones.

[0053] The flash flood inundation hazard level classification method provided in this embodiment constructs a two-dimensional hydrodynamic model to physically simulate the flash flood inundation process under different return periods of designed rainfall scenarios, obtaining the inundation depth distribution results of each spatial unit in the entire watershed under each return period. Based on a preset disaster-causing depth threshold, the disaster-causing inundation range of each return period is extracted and vectorized, establishing a quantitative mapping relationship between the occurrence probability of the rainstorm return period and the hazard level. The classification of flash flood inundation hazard levels is achieved through spatial overlay analysis. The method of this invention, based on physical mechanism simulation and quantitative threshold judgment, establishes a complete technical process from rainfall input to hazard level output, making the hazard level classification results have clear physical meaning and reproducibility, providing scientific and objective decision support for flash flood disaster prevention and control. It solves the problem that existing flash flood hazard level classification methods rely on historical statistics and experience judgment, and cannot quantitatively describe the spatial distribution of inundation depth and the spatiotemporal boundaries of the disaster-causing range during the evolution of flash floods.

[0054] This embodiment provides a method for classifying flash flood inundation hazard levels, which can be used in the aforementioned electronic devices or terminal devices. Figure 3 This is a flowchart of a method for classifying flash flood inundation hazard levels according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain digital elevation model data for the target area and generate design rainfall data with different return periods for the sub-basins of the target area.

[0055] Specifically, step S301 includes: Step S3011: Perform hydrological analysis preprocessing on the digital elevation model data and convert it into raster data that serves as the standard input for the model.

[0056] For example, using 10-meter resolution DEM (Digital Elevation Model) data, a series of hydrological analyses are performed on the DEM data, such as filling depressions, calculating flow direction, and dividing sub-basins, and then the data is converted into the model's standard input format (e.g., .asc file).

[0057] Step S3012: Obtain the hydrological statistical parameters of the sub-basins of the target area, and calculate the design total rainfall for different return periods based on the hydrological statistical parameters and a probability distribution model.

[0058] Specifically, design storms with different return periods are used as the initial input conditions for the flash flood inundation simulation. The calculations include the calculation of the total design rainfall and the allocation of storm time histories. Flash flood gullies are mostly located in areas with no or insufficient data. Due to data limitations, it is difficult to collect long-sequence monitoring data for a single flash flood gully, such as the F gully in this embodiment. Therefore, it is not possible to calculate the design rainfall based on the extension and interpolation of historical rainfall sequences. Therefore, referring to the rainstorm zoning map of City A, the F gully watershed belongs to Zone II. According to the "Rainstorm Atlas of the Hydrological Manual of City A", the coefficient of variation Cs, skewness coefficient Cv, and mean rainfall of each sub-watershed are obtained by referring to the map. A probability distribution model, such as the Pearson-III type curve, is used to calculate the total design rainfall values ​​for different return periods, including four scenarios: 5-year return, 10-year return, 20-year return, and 100-year return.

[0059] Step S3013: Based on the preset rainfall time-history allocation standard, the total design rainfall with different return periods is allocated to each time period according to the weight, generating design rainfall data that changes over time.

[0060] Specifically, such as Figure 4 As shown, based on the local rainfall time-history allocation standard of City A, the design rainstorm process base number is allocated according to weight to obtain the design rainstorm process with different return periods for each mountain torrent gully sub-basin within the watershed, and the rainfall of each time period is rasterized on the watershed surface.

[0061] Step S302: Based on the digital elevation model data and the design rainfall data, a two-dimensional hydrodynamic model is constructed to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period.

[0062] Specifically, step S302 includes: Step S3021: Convert the preprocessed raster data into raster cells and assign an elevation value to each raster cell at the corresponding location; Based on the design rainfall data, calculate the runoff process data of each raster cell during the entire rainfall process using a hydrological model; Based on the raster cells containing elevation values ​​and the runoff process data, generate a computable two-dimensional hydrodynamic model with real terrain information and runoff information for the target area.

[0063] Specifically, this embodiment uses TRITON (Two-dimensional Runoff Inundation Toolkit for Operational Needs), an open-source two-dimensional flood simulation tool specifically designed to simulate flood wave propagation and surface inundation processes. Its core is the two-dimensional shallow water equations, suitable for high-performance computing environments, capable of running on CPU / GPU or hybrid architectures, balancing simulation accuracy and computational efficiency. The model's main functions include: solving for flood propagation and surface inundation processes on regular or structured grids; supporting flow / runoff generated by river hydrological processes or runoff models as input to drive the model; and outputting water depth (depth map), unit outflow distribution, and point-based time-series flood processes at any given time. The mathematical foundation used by the model is the two-dimensional shallow water equations (with source terms), which take the following form:

[0064] in, Indicates water depth. , Represents the horizontal velocity components (along the x and y directions, respectively). Represents gravitational acceleration. Indicates the elevation of the bottom of the ground. , All are friction terms. It is runoff / rainfall input (source term). , Other external forces (such as surface slope drive, runoff input, etc.)

[0065] The equations are solved in TRITON using an explicit time-progression scheme, employing an improved ROE approximation, and improving numerical stability by locally implicitly handling the friction term.

[0066] The example constructs a two-dimensional hydrodynamic model covering the F-gully watershed (including slopes and gullies) based on 10-meter resolution DEM information.

[0067] Step S3022: A two-dimensional hydrodynamic model is used to simulate flash flood inundation and obtain the distribution results of flash flood inundation water depth for each return period.

[0068] In some optional implementations, step S3022 above includes: Step a1: Set the water depth and flow velocity of each grid cell at the start of the simulation to preset values, and set the downstream boundary conditions for free outflow according to the outlet topography of each sub-basin.

[0069] Specifically, the initial conditions of the model are one of the important influencing factors of the two-dimensional hydrodynamic model, and their accuracy directly affects whether the hydrodynamic model can be calculated stably. Since the example watershed is a typical arid and semi-arid region, the multi-year average precipitation is much less than the annual average evaporation, and the mountain torrent gullies are usually in a state of interruption. Therefore, the initial flow condition of the two-dimensional hydrodynamic model is set to 0 cubic meters per second.

[0070] To simulate real flood evolution scenarios, the lower boundary conditions of each river channel in the model are set to free outflow conditions.

[0071] Step a2: Input the runoff generation process data of each return period into the two-dimensional hydrodynamic model time by time to simulate the evolution of the flood in the corresponding sub-basin and obtain the simulation results for each return period.

[0072] Specifically, the input to the constructed two-dimensional hydrodynamic model comes from the grid-distributed runoff results calculated by the runoff generation module of the hydrological model after the runoff generation parameters have been calibrated. The runoff generation process calculated by each grid is then input into the two-dimensional hydrodynamic model.

[0073] Step a3: Extract the maximum water depth value of each grid cell from the simulation results of each return period throughout the entire simulation period, and generate the flash flood inundation water depth distribution results for the corresponding return period.

[0074] Specifically, the maximum water depth value within the entire simulation period is extracted grid by grid from the flash flood inundation simulation results of each return period, generating the spatial distribution result of the maximum inundation water depth of each spatial unit under the corresponding return period, which is used to quantitatively describe the maximum disaster-causing inundation degree that may be caused by rainstorms of different frequencies.

[0075] Step S303: Extract the rasterized disaster-causing inundation results that meet the preset disaster-causing water depth threshold from the inundation depth distribution results of each return period, and vectorize them to obtain the disaster-causing inundation range vector for each return period.

[0076] Step S303 above includes: Step S3031: Extract the maximum water depth value of each grid cell during the entire simulation period from the flash flood inundation simulation results for each return period, and generate the maximum inundation water depth distribution map for the corresponding return period.

[0077] Specifically, the maximum water depth value that occurs throughout the entire simulation period is extracted grid by grid from the flash flood inundation simulation results of each return period, and a spatial distribution map of the maximum inundation water depth of each spatial unit under the corresponding return period is generated to quantitatively describe the maximum disaster-causing inundation degree that may be caused by rainstorms of different frequencies.

[0078] For example, based on the aforementioned hydrological and hydrodynamic model construction results, inundation simulation results covering the entire F-ditch basin were generated. The model uses a 10-meter resolution grid as the smallest calculation unit. Due to the rapid rise and fall of flash floods, the entire process of rising and receding water may be completed within a few hours. Therefore, considering the most unfavorable conditions, the extracted inundation depth is the maximum value of the inundation depth change process of each grid during the simulation period, rather than the inundation depth at the final moment, to ensure that every grid in the entire basin is accurate.

[0079] Step S3032: Based on the preset disaster-causing water depth threshold, the grid cells with water depth values ​​greater than or equal to the corresponding preset disaster-causing water depth threshold in the maximum inundation water depth distribution map are identified as disaster-causing grids, and the gridded disaster-causing inundation results for each return period are extracted and generated.

[0080] Specifically, based on disaster-causing factors such as water depth at which adults slip, a disaster-causing water depth threshold is set. Grids with water depth values ​​greater than or equal to the disaster-causing water depth threshold in the maximum flooding water depth grid map are identified as flooding hazard grids and extracted.

[0081] More specifically, based on a preset disaster-causing water depth threshold, grid cells with water depth values ​​greater than or equal to the threshold in the maximum inundation water depth distribution map for each return period are identified as disaster-causing grids. Binarized grid data for the corresponding return period is extracted and generated to identify the spatial location and range that meet the disaster-causing conditions.

[0082] Based on the calculation of the maximum inundation depth under different return periods in mountainous areas, it is necessary to scientifically determine the inundation range threshold (if the value is greater than the threshold, the grid is considered to be submerged; otherwise, it is considered not to be submerged), so as to generate an inundation range that conforms to scientific and objective laws.

[0083] To reasonably determine the inundation depth threshold, based on previous research, this study considered three types of disaster-bearing entities: population, houses, and vehicles. Each entity was classified into vulnerability levels. By considering the destructive force of water flow on disaster-bearing entities under different combinations of inundation depth and flow velocity, the damage thresholds for different categories and levels of disaster-bearing entities were clarified. Ultimately, the water depth threshold at which an adult would slip and fall (0.2 meters) was used as the threshold for extracting the inundation range. That is, if the maximum inundation depth of a grid cell is greater than 0.2 meters, the cell is considered to be at risk of inundation; if the maximum inundation depth is less than 0.2 meters, the cell is considered not at risk. For households at risk on the edge of the inundation range, factors such as inundation depth, flow velocity, and building structure were introduced to assess the flash flood risk one by one. Based on relevant technical specifications, preliminary results for the inundation range were formed.

[0084] Step S3033: Convert the rasterized disaster inundation results for each return period into vector surface features, and generate disaster inundation range vectors for each return period.

[0085] Specifically, the rasterized inundation results for each return period are converted into vector surface features to generate vector data of the inundation range for the corresponding return period, which is used for subsequent spatial overlay analysis and hazard level classification.

[0086] More specifically, because the edges of the flooding hazard range calculated and extracted based on raster data are jagged, the example uses ArcGIS software to vectorize and smooth the extracted preliminary flooding range results.

[0087] Due to the dramatic changes in mountainous terrain, some villages have local high points or low points, resulting in localized flooding. In some cases, only one house may not be flooded, but the surrounding area may be completely submerged. In the flooding area map, this may appear as a small triangle or scattered spots. Considering that the surrounding area is completely submerged, it is difficult to carry out rescue operations in a timely manner. Therefore, these areas are treated as submerged and included in the flooding danger zone.

[0088] Step S304: Based on the preset mapping relationship between the return period interval and the hazard level, the disaster-causing inundation range vectors with different return periods are spatially superimposed and analyzed to map them into different flash flood inundation hazard levels.

[0089] Specifically, step S304 includes: Step S3041: Sort the disaster-causing inundation range vectors for each return period in ascending order of return period.

[0090] Step S3042: Based on the preset mapping relationship between the return period interval and the hazard level, starting from the disaster-causing inundation range with the lowest return period, subtract all disaster-causing inundation ranges with low return periods from the disaster-causing inundation ranges with high return periods to obtain the independent area for each hazard level.

[0091] Specifically, according to model simulations, the inundation areas for flash floods occurring once every 5 years, 10 years, 20 years, and 100 years, as ultimately determined, show significant runoff generation in the F-ditch watershed under all four rainfall scenarios. Under the 5-year flood scenario, the inundation area is 2.51 km². 2 Under a 10-year flood scenario, the inundated area is 2.84 km². 2 Under a 20-year flood scenario, the inundated area would be 3.61 km². 2 Under a 100-year flood scenario, the inundated area would be 9.07 km². 2 .

[0092] Step S3043: Obtain basic geographic information of the target area, merge the independent areas of each hazard level, and spatially overlay them with the basic geographic information to form a distribution map of flash flood hazard levels.

[0093] In some optional implementations, step S3043 above includes: Step b1: The flooded areas with a return period less than or equal to the first preset threshold are classified as extremely high-risk areas; the flooded areas with a return period greater than the first preset threshold and less than or equal to the second preset threshold are classified as high-risk areas; the flooded areas with a return period greater than the second preset threshold and less than or equal to the third preset threshold are classified as medium-risk areas; and the flooded areas with a return period greater than the third preset threshold and less than or equal to the fourth preset threshold are classified as low-risk areas.

[0094] Specifically, the first threshold is 5 years, the second threshold is 10 years, the third threshold is 20 years, and the fourth threshold is 100 years.

[0095] The inundation areas caused by rainstorms with different return periods are converted into the hazard levels of flash floods. The inundation areas with a return period less than or equal to the first threshold (such as once every 5 years) are designated as extremely high-risk areas; the inundation areas with a return period greater than the first threshold and less than or equal to the second threshold (such as once every 5 to 10 years) are designated as high-risk areas; and so on, to designate four levels of flash flood hazard zones.

[0096] Step b2 involves spatially overlaying the delineated independent areas of different hazard levels with basic geographic information to generate a distribution map of flash flood inundation hazard levels.

[0097] Specifically, by overlaying the results of the danger zone delineation with elements such as administrative villages, important infrastructure, and population distribution maps, a mountain flood inundation danger zone map is formed. This map can directly provide decision support for flood control departments to carry out precise defense against mountain flood disasters, and can generate practical flood control and disaster reduction benefits.

[0098] Based on the simulation results of rainstorms and flash floods under different scenarios, a four-level classification method and visualization technology for flash flood hazard zones are realized to support flood control decision-making and are applicable to the assessment and prevention of flash flood disasters in small watersheds in mountainous areas.

[0099] The flash flood inundation hazard classification method provided in this embodiment is based on a two-dimensional hydrodynamic model with physical mechanisms, commonly used to simulate flood evolution under complex underlying surface conditions. Its core governing equation is the two-dimensional shallow water equation, which can dynamically calculate and output the hydrological processes and hydraulic characteristic values ​​of each grid within the watershed, such as water level and flow rate. It can fully consider the direct impact of spatial heterogeneity in mountainous areas on flash flood evolution. This invention utilizes high-precision DEM grid data to construct a two-dimensional hydrodynamic model, simulating and obtaining the inundation depth of each grid at each time step under multiple design rainfall scenarios. This fully characterizes the dynamic distribution of rainstorms and flash floods at a refined spatiotemporal scale. Furthermore, by setting a mapping rule of "rainstorm recurrence period - flash flood inundation calculation - hazard classification", a complete and reproducible hazard classification technical process from different rainfall levels to flash flood inundation risk is constructed, providing scientific support for the quantitative assessment of flash flood risk and the refinement of flash flood disaster prevention and control.

[0100] As one or more specific application embodiments of the present invention, the method for classifying flash flood inundation hazard levels provided by the present invention will be further described in detail, and the specific process is as follows: Step S1: Data standardization preprocessing, performing a series of hydrological analyses on the DEM data such as depression filling, flow direction calculation, and sub-basin division, and further converting it into the model standard input format (.asc file). Step S2: Multi-scenario design rainfall calculation, generating a set of rainstorm processes with different return periods (e.g., 5, 10, 20, 100 years) for each sub-basin; Step S3: Two-dimensional hydrodynamic model simulation. First, runoff calculation is performed using a hydrological model. The hydrological model is constructed and calibrated. The most unfavorable factors are considered, and the watershed grid distributed runoff results under saturated soil moisture conditions are calculated. Then, a two-dimensional hydrodynamic model is constructed. The grid runoff results are used as input. Initial conditions and boundary conditions are set, and multi-scenario simulation is performed to calculate the maximum inundation depth of each grid cell throughout the simulation process. Step S4: Disaster-causing water depth extraction. Based on the disaster-causing water depth threshold (e.g., 0.2 meters) for typical disaster-bearing bodies (such as people and vehicles) affected by flash floods, the inundation results for the entire time period are processed in batches to extract the rasterized disaster-causing inundation results for each rainfall scenario. Step S5: Spatial analysis of the disaster-causing inundation range. Convert the disaster-causing inundation range raster under each scenario into vector polygons. Based on high-definition remote sensing images and according to the actual distribution of mountain terrain, process the results into smooth surface vector files. Step S6: Delineate the hazard levels of flash flood inundation areas. Establish a direct mapping rule between "rainstorm return period" and "hazard level," converting inundation areas with different return periods into flash flood inundation hazard levels. This includes designating inundation areas with a return period less than or equal to the first threshold (e.g., once every 5 years) as extremely high-risk areas; designating inundation areas with a return period greater than the first threshold but less than or equal to the second threshold (e.g., once every 5 to 10 years) as high-risk areas; and so on. Spatial overlay analysis is used to classify extremely high-risk areas, high-risk areas, medium-risk areas, and low-risk areas.

[0101] The following uses the F-ditch watershed in District B of City A as an example to illustrate the specific process of the present invention.

[0102] Step S1', Data standardization preprocessing: The example uses 10-meter resolution DEM (Digital Elevation Model) data to perform a series of hydrological analyses, such as filling depressions, calculating flow direction, and dividing sub-basins, and then converts the DEM data into the model's standard input format (.asc file).

[0103] Step S2', Multi-scenario Rainfall Calculation Design: Using design storms with different return periods as the initial input conditions for flash flood inundation simulation, the calculations include the calculation of total design rainfall and the allocation of storm time histories. Flash flood gullies are mostly located in areas with no or insufficient data. Due to data limitations, for a single flash flood gully, such as the F gully in this embodiment, it is difficult to collect long-sequence monitoring data, and it is currently impossible to calculate the design rainfall based on the extension and interpolation of historical rainfall sequences. Therefore, referring to the storm zoning map of City A, the F gully basin belongs to Zone II. According to the "City A Hydrological Handbook (Volume 1) Storm Atlas," the Cs, Cv values, and average rainfall values ​​of each sub-basin are obtained from the map. The Pearson-III type curve is used to calculate the total design rainfall values ​​for different return periods, including four scenarios: 5-year, 10-year, 20-year, and 100-year return periods. Furthermore, based on the local standards of City A, the design storm process base is weighted and allocated to obtain the design storm processes for different return periods of each flash flood gully sub-basin within the basin. The rainfall for each time period is then rasterized on the basin surface.

[0104] Step S3', Two-dimensional hydrodynamic model simulation: (1) Model principle: This embodiment utilizes TRITON (Two-dimensional Runoff Inundation Toolkit for Operational Needs), an open-source two-dimensional flood simulation tool specifically designed to simulate flood wave propagation and surface inundation processes. Its core is the two-dimensional shallow water equations, suitable for high-performance computing environments, capable of running on CPU / GPU or hybrid architectures, balancing simulation accuracy and computational efficiency. The model's main functions include: solving for flood propagation and surface inundation processes on regular or structured grids; supporting flow / runoff generated by river hydrological processes or runoff models as input to drive the model; and outputting water depth (depth map), unit outflow distribution, and point-based time-series flood processes at any given time. The mathematical foundation of the model is the two-dimensional shallow water equations (with source terms), in the following form:

[0105] in, Indicates water depth. , Represents the horizontal velocity components (along the x and y directions, respectively). Represents gravitational acceleration. Indicates the elevation of the bottom of the ground. , All are friction terms. It is runoff / rainfall input (source term). , Other external forces (such as surface slope drive, runoff input, etc.)

[0106] The equations are solved in TRITON using an explicit time-progression scheme, employing an improved ROE approximation, and improving numerical stability by locally implicitly handling the friction term.

[0107] The example constructs a two-dimensional hydrodynamic model covering the F-gully watershed (including slopes and gullies) based on 10-meter resolution DEM information.

[0108] (2) Model input: The input to the constructed two-dimensional hydrodynamic model comes from the grid-distributed runoff results calculated by the runoff generation module of the hydrological model after the runoff generation parameters have been calibrated. The runoff generation process calculated by each grid is then input into the two-dimensional hydrodynamic model.

[0109] (3) Initial conditions: The initial conditions of the model are one of the important influencing factors of the two-dimensional hydrodynamic model, and their accuracy directly affects whether the hydrodynamic model can be calculated stably. Since the example watershed is a typical arid and semi-arid region, the multi-year average precipitation is much less than the annual average evaporation, and the mountain torrent gullies are usually in a state of interruption. Therefore, the initial flow condition of the two-dimensional hydrodynamic model is set to 0 cubic meters per second.

[0110] (4) Boundary conditions: In order to simulate the real flood evolution scenario, the lower boundary conditions of each river channel in the model are set to free outflow conditions.

[0111] Step S4', Disaster-causing water depth extraction: Based on the aforementioned hydrological and hydrodynamic model construction results, inundation simulation results covering the entire F-ditch basin were generated. The model uses a 10-meter resolution grid as the smallest calculation unit. Due to the rapid rise and fall of flash floods, the entire process of rising and receding water can be completed within a few hours. Therefore, considering the most unfavorable conditions, the extracted inundation depth is the maximum value of the inundation depth change process of each grid during the simulation period, rather than the inundation depth at the final moment, to ensure the accuracy of the inundation depth at every grid in the entire basin.

[0112] Based on the calculation of the maximum inundation depth under different return periods in mountainous areas, it is necessary to scientifically determine the inundation range threshold (if the value is greater than the threshold, the grid is considered to be submerged; otherwise, it is considered not to be submerged), so as to generate an inundation range that conforms to scientific and objective laws.

[0113] To reasonably determine the inundation depth threshold, based on previous research, this study considered three types of disaster-bearing entities: population, houses, and vehicles. Each entity was classified into vulnerability levels. By considering the destructive force of water flow on disaster-bearing entities under different combinations of inundation depth and flow velocity, the disaster damage thresholds for different categories and levels of disaster-bearing entities were clarified. Ultimately, the water depth threshold at which an adult would slip and fall (0.2 meters) was used as the threshold for extracting the inundation range. That is, if the maximum inundation depth of a grid cell is greater than 0.2 meters, the cell is considered to be at risk of inundation; if the maximum inundation depth is less than 0.2 meters, the cell is considered not at risk. For households at risk on the edge of the inundation range, factors such as inundation depth, flow velocity, and building structure were introduced to assess the flash flood risk one by one. Based on the requirements of the "Technical Specification for Flash Flood Disaster Investigation and Evaluation SL767-2018," preliminary results on the inundation range were formed.

[0114] Step S5', Optimization of flooding range: Because the edges of the flood hazard range calculated and extracted based on raster data are jagged, the example uses ArcGIS software to vectorize and smooth the preliminary flood range results.

[0115] Due to the dramatic changes in mountainous terrain, some villages have local high points or low points, resulting in localized flooding. In some cases, only one house may not be flooded, but the surrounding area may be completely submerged. In the flooding area map, this may appear as a small triangle or scattered spots. Considering that the surrounding area is completely submerged, it is difficult to carry out rescue operations in a timely manner. Therefore, these areas are treated as submerged and included in the flooding danger zone.

[0116] Step S6', the danger zone of flash floods with different return periods: The final flood inundation areas for 5-year, 10-year, 20-year, and 100-year return periods were determined based on model simulations, showing that the F-ditch watershed exhibits significant runoff under all four rainfall scenarios. Under the 5-year return period scenario, the inundation area is 2.51 km². 2 Under a 10-year flood scenario, the inundated area is 2.84 km². 2 Under a 20-year flood scenario, the inundated area would be 3.61 km². 2 Under a 100-year flood scenario, the inundated area would be 9.07 km². 2 .

[0117] The inundation areas caused by torrential rains with different return periods are converted into inundation hazard levels. Inundation areas with a return period less than or equal to the first threshold (e.g., a 5-year return period) are designated as extremely high-risk areas; inundation areas with a return period greater than the first threshold but less than or equal to the second threshold (e.g., a 5-10 year return period) are designated as high-risk areas; and so on, resulting in four levels of inundation hazard zones. The hazard zone designations are overlaid with data on administrative villages, critical infrastructure, and population distribution to create an inundation hazard map. This map directly supports flood control departments in their precise flood disaster prevention efforts, generating tangible flood control and disaster reduction benefits.

[0118] The method for classifying flash flood inundation hazard levels provided in this embodiment has the following beneficial effects: 1) This invention organically integrates high-precision terrain processing, distributed rainfall runoff calculation, two-dimensional hydrodynamic model simulation, GIS spatial analysis, and hazard level mapping rules based on rainstorm recurrence period into a complete technology chain, forming a standardized technology for quantitative classification and delineation of flash flood inundation risk; 2) This invention uses physical simulation of hydrodynamics as its core, and employs clear frequency mapping rules and physical disaster thresholds as the basis for judgment, minimizing the influence of subjective experience on the classification results. The flash flood inundation hazard level classification results generated using this technology can directly reflect the probability of flash flood inundation and the potential impact range of different spatial locations and areas. The conclusions are scientific and objective, and the technical methods are verifiable and scalable.

[0119] 3) The quantitative classification rules for "rainstorm recurrence period" and "flash flood hazard level" included in this invention (e.g., flash flood inundation area with rainstorms less than once in 5 years → extremely high hazard area, flash flood inundation area with rainstorms once in 5-10 years → high hazard area, flash flood inundation area with rainstorms once in 10-20 years → medium hazard area, flash flood inundation area with rainstorms once in 20-100 years → low hazard area) have clear probability significance and clear mathematical logic, overcoming the shortcomings of traditional qualitative methods such as full-domain risk judgment, vague risk definition, and reliance on experience for classification.

[0120] 4) In the final generated "Distribution Map of Mountain Flood Inundation Hazard Levels," the "Extremely High Danger Zones" directly correspond to areas with a high frequency of rainstorms and floods. These areas are key protection targets and priority early warning and evacuation targets during the flood season. This method of hazard level classification is highly compatible with the "tiered control and tiered response" emergency management model of local flood control departments. The results based on this invention can be directly provided to local water resources emergency flood control departments, providing clear scientific basis and practical value for refined mountain flood prevention work such as identifying early warning targets, planning evacuation routes, selecting resettlement sites, and developing mountain flood disaster prevention plans.

[0121] This embodiment also provides a flash flood hazard level classification device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0122] This embodiment provides a device for classifying the risk level of flash flood inundation, such as... Figure 5 As shown, it includes: The rainfall calculation module 501 is used to acquire digital elevation model data of the target area and generate design rainfall data with different return periods for the sub-basins of the target area.

[0123] The hydrodynamic simulation module 502 is used to construct a two-dimensional hydrodynamic model based on digital elevation model data and design rainfall data to simulate flash flood inundation and obtain the distribution results of flash flood inundation water depth at each return period.

[0124] The disaster extraction module 503 is used to extract the rasterized disaster inundation results that meet the preset disaster inundation depth threshold from the inundation depth distribution results of each return period, and vectorize them to obtain the disaster inundation range vector of each return period.

[0125] The spatial analysis and classification module 504 is used to perform spatial overlay analysis on the disaster-causing inundation range vectors with different return periods based on the preset mapping relationship between the return period interval and the hazard level, and map them into different flash flood inundation hazard levels.

[0126] In some alternative implementations, the rainfall calculation module 501 includes: The preprocessing unit is used for hydrological analysis preprocessing of digital elevation model data, converting it into raster data that serves as the standard input for the model.

[0127] In some alternative implementations, the rainfall calculation module 501 further includes: The design unit for calculating total rainfall is used to obtain the hydrological statistical parameters of the sub-basins of the target area, and based on the hydrological statistical parameters, a probability distribution model is used to calculate the design total rainfall for different return periods.

[0128] The rainfall calculation unit is used to allocate the total design rainfall with different return periods to each time period according to the weight based on the preset rainfall time history allocation standard, and generate design rainfall data that changes over time.

[0129] In some alternative implementations, the hydrodynamic simulation module 502 includes: The hydrodynamic model building unit is used to convert preprocessed raster data into raster cells and assign an elevation value to each raster cell at a corresponding location. Based on the design rainfall data, the runoff generation process data of each raster cell during the entire rainfall process is calculated through the hydrological model. Based on the raster cells containing elevation values ​​and the runoff generation process data, a computable two-dimensional hydrodynamic model with real terrain information and runoff generation information is generated for the target area.

[0130] The flash flood inundation simulation unit is used to simulate flash flood inundation using a two-dimensional hydrodynamic model, and obtain the flash flood inundation depth distribution results for each return period.

[0131] In some alternative implementations, the flash flood simulation unit includes: The boundary condition setting sub-unit is used to set the water depth and flow velocity of each grid cell at the start of the simulation to preset values, and to set the downstream boundary conditions for free outflow based on the outlet topography of each sub-basin.

[0132] The input condition setting sub-unit is used to input the runoff generation process data of each return period into the two-dimensional hydrodynamic model time by time to simulate the evolution process of flood in the corresponding sub-basin and obtain the simulation results for each return period.

[0133] The water depth distribution generation sub-unit is used to extract the maximum water depth value of each grid cell during the entire simulation period from the simulation results of each return period, and generate the flash flood inundation water depth distribution results for the corresponding return period.

[0134] In some alternative implementations, the disaster recovery module 503 includes: The maximum inundation depth extraction unit is used to extract the maximum water depth value of each grid cell from the flash flood inundation simulation results of each return period throughout the entire simulation period, and generate the maximum inundation depth distribution map of the corresponding return period.

[0135] The disaster-causing water depth extraction unit is used to determine the grid cells with water depth values ​​greater than or equal to the corresponding preset disaster-causing water depth threshold in the maximum inundation water depth distribution map as disaster-causing grid cells based on the preset disaster-causing water depth threshold, and extract and generate the gridded disaster-causing inundation results for each return period.

[0136] Vectorization units are used to convert rasterized flooding results for each return period into vector surface features, generating flooding range vectors for each return period.

[0137] In some alternative implementations, the spatial analysis and classification module 504 includes: The sorting unit is used to sort the disaster-causing inundation range vectors for each return period from smallest to largest according to the return period.

[0138] The inundation range determination unit is used to determine the independent area for each hazard level by subtracting all the low-recurrence-time inundation ranges from the high-recurrence-time inundation ranges based on a preset mapping relationship between the return period interval and the hazard level.

[0139] The classification unit is used to obtain basic geographic information of the target area, merge the independent areas of each hazard level, and spatially overlay them with the basic geographic information to form a distribution map of flash flood hazard levels.

[0140] In some alternative implementations, the hierarchy unit includes: The classification sub-unit is used to classify the disaster-causing inundation area with a recurrence period less than or equal to the first preset threshold as an extremely high-risk area; the disaster-causing inundation area with a recurrence period greater than the first preset threshold and less than or equal to the second preset threshold as a high-risk area; the disaster-causing inundation area with a recurrence period greater than the second preset threshold and less than or equal to the third preset threshold as a medium-risk area; and the disaster-causing inundation area with a recurrence period greater than the third preset threshold and less than or equal to the fourth preset threshold as a low-risk area.

[0141] The spatial analysis sub-unit is used to spatially overlay the delineated independent areas of different hazard levels with basic geographic information to generate a distribution map of flash flood hazard levels.

[0142] The flash flood hazard level classification device provided in this embodiment of the invention can execute the flash flood hazard level classification method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0143] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0144] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0145] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0146] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the flash flood hazard level classification method of the embodiments of the present invention.

[0147] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0148] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the flash flood hazard level classification method shown in the above embodiments is implemented.

[0149] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0150] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for classifying the hazard level of flash flood inundation, characterized in that, The method includes: Acquire digital elevation model data for the target area and generate design rainfall data with different return periods for the sub-basins of the target area; Based on the digital elevation model data and the design rainfall data, a two-dimensional hydrodynamic model is constructed to simulate flash flood inundation and obtain the flash flood inundation depth distribution results for each return period. From the inundation depth distribution results of each return period, the rasterized disaster-causing inundation results that meet the preset disaster-causing depth threshold are extracted and vectorized to obtain the disaster-causing inundation range vector for each return period; Based on the pre-defined mapping relationship between the return period interval and the hazard level, the vector of the disaster-causing inundation range with different return periods is spatially superimposed and analyzed to map it into different flash flood inundation hazard levels.

2. The method according to claim 1, characterized in that, The acquisition of digital elevation model data for the target area includes: The digital elevation model data is preprocessed using hydrological analysis and then converted into raster data, which is the standard input for the model.

3. The method according to claim 1, characterized in that, Generate design rainfall data with different return periods for sub-basins of the target area, including: Obtain the hydrological statistical parameters of the sub-basins of the target area, and based on the hydrological statistical parameters, use a probability distribution model to calculate the design total rainfall for different return periods; Based on the preset rainfall time-history allocation standard, the total design rainfall with different return periods is allocated to each time period according to the weight, generating design rainfall data that changes over time.

4. The method according to claim 2, characterized in that, Based on the digital elevation model data and the design rainfall data, a two-dimensional hydrodynamic model is constructed to simulate flash flood inundation, obtaining the flash flood inundation depth distribution results for each return period, including: The preprocessed raster data is converted into raster cells, and each raster cell is assigned an elevation value corresponding to its location. Based on the designed rainfall data, the runoff generation process data of each grid cell during the entire rainfall process are calculated using a hydrological model; Based on grid cells containing elevation values ​​and runoff process data, a computable two-dimensional hydrodynamic model with real terrain and runoff information is generated for the target area. The two-dimensional hydrodynamic model was used to simulate flash flood inundation, and the distribution of flash flood inundation depth at each return period was obtained.

5. The method according to claim 3, characterized in that, The two-dimensional hydrodynamic model was used to simulate flash flood inundation, and the distribution of flash flood inundation depth at various return periods was obtained, including: The water depth and flow velocity of each grid cell at the start of the simulation are set to preset values, and the downstream boundary conditions for free outflow are set according to the outlet topography of each sub-basin. The runoff generation process data for each return period are input into the two-dimensional hydrodynamic model time by time to simulate the evolution of the flood in the corresponding sub-basin and obtain the simulation results for each return period. From the simulation results of each return period, the maximum water depth value of each grid cell during the entire simulation period is extracted to generate the flash flood inundation water depth distribution results for the corresponding return period.

6. The method according to claim 1, characterized in that, The step of extracting rasterized disaster-causing inundation results that meet the preset disaster-causing depth threshold from the inundation depth distribution results of each return period, and vectorizing them to obtain the disaster-causing inundation range vector for each return period, includes: From the flash flood inundation simulation results of each return period, the maximum water depth value of each grid cell during the entire simulation period is extracted to generate the maximum inundation water depth distribution map for the corresponding return period; Based on the preset disaster-causing water depth threshold, grid cells with water depth values ​​greater than or equal to the corresponding preset disaster-causing water depth threshold in the maximum inundation water depth distribution map are identified as disaster-causing grids, and gridded disaster-causing inundation results for each return period are extracted and generated. The rasterized inundation results for each return period are converted into vector surface features, generating inundation range vectors for each return period.

7. The method according to claim 1, characterized in that, The preset mapping relationship between return period intervals and hazard levels involves spatially overlaying and analyzing the disaster-causing inundation range vectors with different return periods, mapping them to different flash flood inundation hazard levels, including: The disaster-causing inundation range vectors for each return period are sorted from smallest to largest according to the return period; Based on the pre-defined mapping relationship between the return period interval and the hazard level, starting from the disaster-causing inundation range with the lowest return period, the disaster-causing inundation range with the highest return period is subtracted from the disaster-causing inundation range with the lowest return period to obtain the independent area for each hazard level. The basic geographic information of the target area is obtained, the independent areas of each hazard level are merged, and the data is spatially overlaid with the basic geographic information to form a distribution map of flash flood hazard levels.

8. The method according to claim 7, characterized in that, The independent areas for each hazard level are merged and spatially overlaid with basic geographic information to form a flash flood hazard level distribution map, including: The disaster-causing inundation area with a recurrence period less than or equal to the first preset threshold is classified as an extremely high-risk area; the disaster-causing inundation area with a recurrence period greater than the first preset threshold and less than or equal to the second preset threshold is classified as a high-risk area; the disaster-causing inundation area with a recurrence period greater than the second preset threshold and less than or equal to the third preset threshold is classified as a medium-risk area; and the disaster-causing inundation area with a recurrence period greater than the third preset threshold and less than or equal to the fourth preset threshold is classified as a low-risk area. By spatially overlaying the delineated independent areas of different hazard levels with basic geographic information, a distribution map of flash flood inundation hazard levels is generated.

9. A device for classifying the risk level of flash flood inundation, characterized in that, The device includes: The rainfall calculation module is used to acquire digital elevation model data of the target area and generate design rainfall data with different return periods for the sub-basins of the target area; The hydrodynamic simulation module is used to construct a two-dimensional hydrodynamic model based on the digital elevation model data and the design rainfall data, so as to simulate flash flood inundation and obtain the flash flood inundation water depth distribution results for each return period. The disaster extraction module is used to extract rasterized disaster inundation results that meet the preset disaster inundation depth threshold from the inundation depth distribution results of each return period, and vectorize them to obtain the disaster inundation range vector of each return period. The spatial analysis and classification module is used to perform spatial overlay analysis on the disaster-causing inundation range vectors with different return periods based on the preset mapping relationship between return period intervals and hazard levels, and map them into different flash flood inundation hazard levels.

10. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for classifying flash flood hazard levels as described in any one of claims 1 to 8.

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