Urban waterlogging evaluation method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of urban inland inundation early warning, and provides an urban inland inundation waterlogging evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining low-resolution DEM data and geographic vector data of a target urban region; the geographic vector data comprises road vector data and river vector data; performing resampling on the low-resolution DEM data to obtain high-resolution DEM data; performing correction processing on the road vector data based on the high-resolution DEM data so as to perform elevation sampling on a starting point and an end point of a main road and perform elevation sampling on a starting point and an end point of a non-main road to obtain road elevation data; performing elevation correction on the river vector data to obtain river elevation data; performing flooding depth calculation according to the road elevation data and the river elevation data; and performing flooding risk assessment based on the calculated flooding depth. According to the method, the accuracy of calculating the waterlogging depth of the urban inland inundation can be improved, so that the accuracy of risk early warning evaluation is improved.

Description

Technical Field

[0001] This application relates to the field of urban flooding early warning technology, and in particular to an urban flooding assessment method, device, electronic equipment and storage medium. Background Technology

[0002] Urban flooding is a major challenge in the global urbanization process. Frequent extreme rainfall events exacerbate flood risks, leading to loss of life, property, and economic damage. Currently, the proportion of impermeable surfaces (such as roads and rooftops) in cities is increasing significantly, resulting in a sharp rise in the surface runoff coefficient. Therefore, simulation and risk assessment of urban flooding are necessary.

[0003] Traditional urban flooding simulations rely on digital elevation models (DEMs) with a resolution typically of 30 meters, which is insufficient to accurately reflect the subtle elevation changes in complex urban terrain (such as roads and rivers). For example, vegetation cover or buildings can create false elevation barriers in the DEM, affecting the accuracy of water flow path simulations. Furthermore, the original DEM does not differentiate between road classifications and river conditions during high-water seasons, leading to simulation results that deviate from reality. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method, apparatus, electronic device, and storage medium for assessing urban flooding, which can improve the accuracy of urban flooding simulation.

[0005] The first aspect of this application provides a method for assessing urban flooding, the method comprising: Acquire low-resolution DEM data and geographic vector data of the target city area; the geographic vector data includes road vector data and river vector data, and the roads are divided into main roads and non-main roads according to the road type field; The low-resolution DEM data is resampled to obtain high-resolution DEM data; Based on the high-resolution DEM data, the road vector data is corrected by sampling the elevation of the starting and ending points of the main roads and the starting and ending points of the non-main roads to obtain road elevation data. Elevation correction is performed on the river vector data to obtain the river elevation data; Flooding depth is calculated based on road elevation data and river elevation data; Flood risk assessment is conducted based on the calculated flood depth.

[0006] Optionally, the step of correcting the road vector data based on the high-resolution DEM data to perform elevation sampling of the starting and ending points of major roads and the starting and ending points of non-major roads, to obtain road elevation data, includes: Extract the first DEM elevation values ​​corresponding to the starting coordinates and ending coordinates of the main roads from the low-resolution DEM data; The buffer width of the non-main road is created based on the road width field, and the second DEM elevation values ​​corresponding to the start and end coordinates of the buffer boundary are extracted from the low-resolution DEM data respectively. The sampling points of each road in the main roads are interpolated based on the first DEM elevation value, and the sampling points of each road in the non-main roads are interpolated based on the second DEM elevation value to obtain road elevation data.

[0007] Optionally, the interpolation process for the sampling points of each road in the main roads based on the first DEM elevation value includes: Using the road ID as the grouping key, the sampling points of each of the main roads and the non-main roads are grouped: Determine whether the number of road points in each group is greater than or equal to 2; When the number of road points in each group is less than 2, skip the interpolation process and retain the original sampling points; When the number of road points in each group is greater than or equal to 2, for the main road, calculate the first distance between the starting coordinates and the ending coordinates of the main road; The elev_interp value of each group of road points in the main road is calculated by linear interpolation based on the first DEM elevation value and the first distance.

[0008] Optionally, the interpolation process for the sampling points of each road in the non-major roads based on the second DEM elevation value includes: The nearest neighbor algorithm is used to match the nearest main road point in the main road; Copy the first DEM elevation value of the nearest major road point as the reference DEM elevation value for each group of road points on the non-major roads; Calculate the second distance between the starting coordinates and the ending coordinates of the buffer boundary; The elev_interp value of each group of road points in the non-major road is calculated by linear interpolation based on the second DEM elevation value, the reference DEM elevation value, and the second distance.

[0009] Optionally, the elev_interp value of each group of road points in the non-major roads is calculated using the following formula:

[0010] in, The `elev_interp` value for each group of road points in the non-major roads. Starting point The corresponding second DEM elevation value, End point The corresponding second DEM elevation value, and To accumulate distance, Accumulate the distance to the current point; Calculate using the following formula and :

[0011] in, For the nearest major road point, The elevation value of the reference DEM; The nearest major road point is determined by the following formula:

[0012] For the road point set of the corrected main roads, For the set of road points of the non-major roads to be corrected, This is the second distance.

[0013] Optionally, the elevation correction of the river vector data to obtain river elevation data includes: Convert the river surface vector data in the river vector data into vector line data; On the vector line data, corresponding sampling points are uniformly generated according to a preset equidistant interval; The nearest shore point to each sampling point is determined using a spatial interpolation algorithm, and the third DEM elevation value of the nearest shore point is determined. The river surface elevation is corrected based on the third DEM elevation value to obtain the river elevation data.

[0014] Optionally, the calculation of flood depth based on road elevation data and river elevation data includes: Land cover type data and rainfall parameters are obtained from the high-resolution DEM data; the land cover type data includes land area and corresponding surface runoff coefficient, and the rainfall parameters include rainfall intensity and corresponding duration. Calculate the effective runoff area based on the surface area and the surface runoff coefficient; Calculate the rainfall depth based on the rainfall intensity and duration; Calculate the runoff volume based on the effective runoff area and the rainfall depth; The flood depth is iteratively calculated based on the runoff volume to generate a flood depth raster map, where each pixel value represents the flood depth of that pixel.

[0015] A second aspect of this application provides an urban flooding assessment device, the device comprising: The acquisition module is used to acquire low-resolution DEM data and geographic vector data of the target city area; the geographic vector data includes road vector data and river vector data, and the roads are divided into main roads and non-main roads according to the road type field. The resampling module is used to resample the low-resolution DEM data to obtain high-resolution DEM data; The road correction module is used to correct the road vector data based on the high-resolution DEM data, to sample the elevation of the starting and ending points of the main roads and the starting and ending points of the non-main roads, so as to obtain road elevation data. The river correction module is used to correct the elevation of river vector data to obtain river elevation data. The flood assessment module is used to calculate flood depth based on road elevation data and river elevation data, and to conduct flood risk assessment based on the calculated flood depth.

[0016] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the urban flooding assessment method.

[0017] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described urban flooding assessment method.

[0018] In summary, the urban flooding assessment method, apparatus, electronic equipment, and storage medium provided in this application have at least one of the following beneficial effects: 1. After acquiring low-resolution DEM data of the target urban area, it is resampled into high-resolution DEM data. High-resolution DEM data can present the undulations and features of the urban terrain in more detail, thus reflecting the subtle elevation changes of the complex urban terrain more accurately and overcoming the problem of low resolution of traditional DEMs.

[0019] 2. Based on high-resolution DEM data, the road vector data is corrected and processed. Roads are divided into main roads and non-main roads according to the road type field, and the elevation of their starting and ending points is sampled. This can obtain the actual elevation information of the roads more accurately, avoid the influence of false elevation obstacles caused by vegetation or buildings on the water flow path simulation, and improve the accuracy of water flow path simulation.

[0020] 3. By classifying roads into primary and secondary roads based on the road type field and sampling the elevations of their starting and ending points, the actual elevations of roads of different grades can be more accurately reflected. At the same time, elevation correction is applied to the river vector data to make it more consistent with the actual state of rivers during floods, thus making the simulation results closer to reality. This solves the problem that the original DEM did not distinguish between road grades and river flood conditions, which caused the simulation results to deviate from reality.

[0021] 4. By acquiring low-resolution DEM data and geographic vector data, and after processing such as resampling, road correction, and river correction, flood depth calculation is performed. This method does not require a large amount of detailed pipe network geometry data and roughness coefficients, thus reducing computational costs. Furthermore, it does not rely on extensive manual preprocessing, making it more efficient for large-scale urban areas. This solves the problems of traditional methods, achieves low-cost and high-efficiency urban flood assessment, and improves the accuracy of urban flood simulation. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an urban flooding assessment method according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the target city area and the corresponding input dataset as shown in an embodiment of this application; Figure 3 This is a schematic diagram illustrating an elevation anomaly region as shown in an embodiment of this application; Figure 4 This is a flowchart illustrating the data preparation and elevation sampling steps in an automated road correction algorithm according to an embodiment of this application; Figure 5 This is a flowchart illustrating the grouping difference and final output steps in an automated road correction algorithm according to an embodiment of this application; Figure 6 This is a schematic diagram illustrating a road elevation sampling method as shown in an embodiment of this application; Figure 7 This is a schematic diagram illustrating a river flooding situation before it was repaired, as shown in an embodiment of this application; Figure 8 This is a schematic diagram illustrating a modified high-resolution DEM according to an embodiment of this application; Figure 9This is a schematic diagram illustrating the changes in flow direction in a riverless area and a river-containing area before and after DEM processing, as shown in an embodiment of this application. Figure 10 This is a schematic diagram illustrating the simulation results of flooding in a riverless area, as shown in an embodiment of this application; Figure 11 This is a schematic diagram illustrating the simulation results of flooding in a river area, as shown in an embodiment of this application; Figure 12 This is a schematic diagram illustrating a flooding situation where a bridge layer covers a river layer, as shown in an embodiment of this application; Figure 13 This is a functional block diagram of an urban flooding assessment device shown in an embodiment of this application; Figure 14 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Furthermore, all connections / linkages involved in the patent do not simply refer to direct contact between components, but rather to the ability to form a better connection structure by adding or reducing connecting accessories according to specific implementation conditions. The various technical features in this invention can be combined interactively without contradicting each other.

[0025] Traditional urban flooding simulations rely on digital elevation models (DEMs) with a resolution typically of 30 meters, which is insufficient to accurately reflect subtle elevation changes in complex urban terrain (such as roads and rivers). For example, vegetation cover or buildings can introduce false elevation barriers into the DEM, affecting the accuracy of water flow path simulations. Furthermore, the original DEMs do not differentiate between road classes and river flooding conditions, leading to simulation results that deviate from reality. For instance, the elevation of narrow roads (such as secondary roads) may be obscured by vegetation or buildings, while river elevations may use data from the dry season, failing to reflect the overflow process during flooding. In addition, while traditional hydrodynamic models (such as solving the Saint-Venant equations) can simulate drainage networks with high accuracy, they rely on detailed pipe network geometry data and roughness coefficients, resulting in high computational costs and limited applicability to areas with scarce data. Some studies have manually corrected DEM elevations (e.g., Khalid et al., 1325), but this relies on manual preprocessing, which is costly and inefficient, hindering rapid deployment in large-scale urban areas. Therefore, a low-cost, high-efficiency method for urban flooding assessment is needed to improve the accuracy of urban flooding simulations.

[0026] Reference Figure 1 The diagram shown is a flowchart illustrating an urban flooding assessment method according to an embodiment of this application. The urban flooding assessment method includes the following steps.

[0027] S11: Obtain low-resolution DEM data and geographic vector data for the target city area.

[0028] The target urban area is the research object for urban flooding simulation. In some embodiments, the electronic device can acquire low-resolution digital elevation model (DEM) data (also known as raw DEM data). The low-resolution DEM data is raster data with a resolution of 30 meters or lower.

[0029] Reference Figure 2 As shown, to facilitate understanding of the inventive concept of this application, the target urban area in the embodiments of this application is the CBD area between Dongcheng District and Chaoyang District of Beijing (see...). Figure 2 The explanation will be based on the pink layer in section (b).

[0030] For any target urban area, during the data preparation phase, electronic devices can acquire geographic vector data, including but not limited to: road vector data, river vector data, and building vector data, while simultaneously acquiring low-resolution DEM data. Figure 2As shown in section (c), the green layer covers an area near open channels or rivers, where drainage is primarily done through open channels. The remaining areas mainly rely on sewer pipes for drainage, requiring water to be piped into the channels. In this embodiment, the low-resolution DEM data comes from the Landsat 8 satellite digital elevation model data from the U.S. Geological Survey (USGS) website (see...). Figure 2 (Part d) of the DEM data, with an original resolution of 30 meters. Specifically, data from September is selected, as rainfall in Beijing gradually decreases and the water level in the catchment area gradually drops. In subsequent corrections, it is important to adjust the water level to the high-water season elevation to better simulate the impending flooding.

[0031] Meanwhile, electronic devices can obtain rainfall scenarios and parameters for the target city area through data released by the China Meteorological Administration, in order to identify areas in the target city area that are also at risk of urban flooding and flash floods.

[0032] S12, resample the low-resolution DEM data to obtain high-resolution DEM data.

[0033] Because the low-resolution DEM data collected does not reflect the generally low elevation of roads in some areas, for example... Figure 3 The horizontal main roads marked within the box are examples of elevation anomaly areas (Jianguomenwai Street) marked with black boxes. Figure 3 Part (a) is the refined digital elevation model. Figure 3 Part (b) shows a satellite image of the high-altitude anomaly area. Therefore, electronic equipment needs to resample the low-resolution DEM data into high-resolution DEM data. Specifically, based on a semi-automatic mode using Python code, the resampling tool in ArcMap 10.8 is used to fuse and refine vector features such as roads and rivers in the 30m low-resolution DEM to a 1-meter resolution, and to divide a 30m×30m pixel into 900 1m×1m smaller pixels. From... Figure 2 Part (c) roughly shows that the target city area, with its higher-elevation buildings and residential areas, is divided into blocks by lower-lying features such as roads or rivers. Additionally, the downloaded DEM data shows the surface elevation of features in the image; for example, the bridge layer over a river will cover the river layer, causing the river surface to be obscured and affecting flooding analysis. If studying the flooding situation of the target city area, the river layer needs to cover the road layer over the bridge over the river, based on the following layer overlay order:

[0034] In the formula, the order from left to right represents the layer coverage order from bottom to top.

[0035] S13, Based on the high-resolution DEM data, the road vector data is corrected by sampling the elevation of the starting and ending points of the main roads and the starting and ending points of the non-main roads to obtain road elevation data.

[0036] For the 1m resolution DEM (i.e., high-resolution DEM data) after cropping, because the width of some roads and rivers is smaller than the side length of a large pixel composed of 30×30 1m pixels, vegetation, bridges, and buildings are mixed with roads and displayed within a single pixel, resulting in the incomplete display of road and river elevations. To meet the accuracy requirements of urban flooding simulation, the elevations of the 1m high-resolution DEM generated by the resampling method need to be corrected. In this embodiment, the electronic device uses automated correction to correct the road vector data, comprising two stages: data preparation and elevation sampling (first stage), and group interpolation and final output (second stage). (Refer to...) Figure 4 The first stage involves acquiring the road and DEM layers and adding a `width` field (road width field) to the road data. Next, the `type` field (road type field) is checked. If the type is "trunk," "trunk link," "primary," "primary link," "secondary," or "secondary link," points are generated at 10-meter intervals along the line, and a `main` field is created with a value of 1, indicating a primary road. If the type is "tertiary," the width is set to 6 meters; for other types, it's set to 3 meters. Then, a buffer is created based on the width value, and the area buffer is converted to a line. Points are again generated at 10-meter intervals along the line, but this time the `main` field value is set to 0 for these points, indicating a non-primary road. Afterward, `distance`, `elev_dem`, and `elev_interp` fields are added to all points, and elevation values ​​sampled from the DEM are assigned to the `elev_dem` field. (Refer to...) Figure 5The second stage involves grouping points by road ID. If the number of points is insufficient, interpolation is skipped, and the data is added to the ArcGIS project. If the number of points is sufficient, the `main` field value of each point is further checked. For points with a `main` field of 1 (main roads), points are first sorted by distance, then the DEM elevation of the start or end point is obtained, the total distance and interpolation ratio are calculated, and finally, the `elev_interp` value is calculated using linear interpolation. For points with a `main` field of 0 (non-main roads), points are also first sorted by distance, but the nearest neighbor interpolation algorithm is used to find the nearest point with a `main` field of 1. Its `elev_dem` value is copied as the new DEM elevation of the start or end point, then the total distance and interpolation ratio are calculated, and then `elev_interp` is calculated using linear interpolation. Finally, all processed points are added to the ArcGIS project, completing the entire elevation value processing workflow.

[0037] Specifically, the `type` field in road vector data includes field types such as "trunk," "primary," "secondary," "tertiary," "bridleway," "construction," "cycleway," and "footway." Electronic devices can assign different widths to roads based on their type. Road vector data containing the fields "trunk," "primary," and "secondary" requires multiple line features when representing the road layer (see...). Figure 6 Part (a) shows a schematic diagram of elevation sampling at the start and end points of a wide road. This means that a two- or four-lane road will have a corresponding number of line features. Assuming this type of road is a main road (also called a trunk road or arterial road), no buffer zone needs to be established. For road vector data containing other types of fields, only one line feature is used when representing the road layer (see...). Figure 6 Part (a) requires a road buffer zone to represent this type of road, which is called a non-tertiary road (also known as a secondary road or branch road). Non-tertiary roads consist of two levels: roads with the "tertiary" field, which have a width of 6m, and roads with the remaining fields, which have a width of 3m.

[0038] Therefore, the roads in the entire target city area can be divided into three types, which can be expressed as:

[0039] in, ={"trunk", "trunk link", "primary", "primary link", "secondary", "secondary link"}, to ensure differentiated treatment of main roads and secondary roads.

[0040] For main roads, elevation sampling is first performed on the starting points and key points of the main roads, referring to... Figure 6 Part (a) is shown in the middle. Because its width is sufficient, 30×30m DEM pixels with a resolution of 1m are covered by this road, potentially directly representing the elevation of the entire road. Therefore, the elevation values ​​of these pixels, i.e., the first DEM elevation values, are directly referenced during correction. For non-major roads, a buffer zone is first constructed based on the road width, and then elevation sampling is performed at the start and end points of the buffer zone, referring to… Figure 6 Part (b) is a schematic diagram of elevation sampling at the start and end points of the narrow road. Because the narrow road is much narrower than 30m, it cannot be directly displayed in the initial data. The pixels contain both roads and buildings, and the resolution of the remote sensing image is low, making it impossible to determine the road's exact width. Therefore, the electronic equipment cannot determine the true elevation of the narrow road based on the initial data, and the elevation sampling follows the normal sampling method. In subsequent corrections, it is only necessary to reflect the relative elevation of the road and adjacent buildings, modifying the road to the lower elevation point used in the flooding analysis.

[0041] In an optional implementation, interpolating the sampling points of each road in the main road based on the first DEM elevation value includes: Using the road ID as the grouping key, the sampling points of each of the main roads and the non-main roads are grouped: Determine whether the number of road points in each group is greater than or equal to 2; When the number of road points in each group is less than 2, skip the interpolation process and retain the original sampling points; When the number of road points in each group is greater than or equal to 2, for the main road, calculate the first distance between the starting coordinates and the ending coordinates of the main road; The elev_interp value of each group of road points in the main road is calculated by linear interpolation based on the first DEM elevation value and the first distance.

[0042] In some embodiments, reference is made together with Figure 5 Based on strict conditional judgment, that is, firstly grouping by road ID and checking whether the number of points in each group is ≥2, to ensure that no single point cannot form an interpolation relationship. When the point number condition is met (i.e., the number of points in each group is ≥2), the interpolation is further selected based on the value of the main field.

[0043] When the point is located on the main road (main=1), the elev_interp value of each group of road points on the main road can be calculated using the linear interpolation method. Its mathematical expression is:

[0044] in, and These represent the first DEM elevation values ​​at the starting and ending points, respectively. and To accumulate distance, Accumulate the distance to the current point. The elev_interp value is the value of each group of road points in the non-major roads.

[0045] In this embodiment, it is assumed that the elevation changes linearly along the road, which simplifies the calculation and maintains the continuity of elevation changes. The distance field uses a cumulative calculation model (where, (representing Euclidean distance):

[0046] In an optional implementation, interpolating the sampling points of each road in the non-major roads based on the second DEM elevation value includes: The nearest neighbor algorithm is used to match the nearest main road point in the main road; Copy the first DEM elevation value of the nearest major road point as the reference DEM elevation value for each group of road points on the non-major roads; Calculate the second distance between the starting coordinates and the ending coordinates of the buffer boundary; The elev_interp value of each group of road points in the non-major road is calculated by linear interpolation based on the second DEM elevation value, the reference DEM elevation value, and the second distance.

[0047] For branch points (main=0), nearest neighbor interpolation is first applied to the start and end points to find the nearest main road point, called the nearest main road point. Next, its elevation value is copied as the baseline DEM elevation value, and then linear interpolation is performed on this basis. In this embodiment, a progressive spatial algorithm for narrow road elevation correction is adopted, which is a progressive road elevation correction algorithm based on the principle of spatial proximity. That is, using the already corrected elevation data of the main roads, the correction results of the main roads (wide roads) are propagated to the surrounding road (narrow road) network using spatial interpolation methods, ultimately achieving automated batch correction of narrow road elevations over a large area. Based on the assumption of terrain continuity, that is, the elevation changes in adjacent areas are progressive and continuous, the algorithm is as follows: This is the corrected set of main road points (the set of road points for the main roads). This is the set of auxiliary road points (the set of road points on non-main roads) to be corrected. For the starting points on the auxiliary roads... and the end point Its elevation value (i.e., the second DEM elevation value). and It can be determined using the nearest neighbor interpolation algorithm:

[0048] in, yes Gather at the main road point The nearest major road point in the middle, The elevation value is the baseline DEM value.

[0049] The nearest major road point is determined using the following formula:

[0050] in, This represents the Euclidean distance between two points.

[0051] By performing linear interpolation based on the corrected start and end points, the elev_interp value of each group of road points in non-major roads can be calculated: ; The `elev_interp` value for each group of road points in the non-major roads. Starting point The second DEM elevation value, End point The second DEM elevation value; This is the second distance.

[0052] The above interpolation method allows narrow roads to reflect relatively accurate elevations, and ensures the consistency between the elevations of branch lines and main lines.

[0053] Import all processed sampling points (including the elev_interp field) into the ArcGIS project to generate a continuous road elevation model containing corrected road elevation data.

[0054] S14, perform elevation correction on the river vector data to obtain the river elevation data.

[0055] Because low-resolution DEM data of river elevations, due to their low resolution and the inclusion of information such as riverbank vegetation and buildings, cannot accurately reflect the river surface height required for urban flooding simulation, the river's elevation is insufficient. Drainage in river areas relies on the relative elevation difference between the land surface and the river surface, which can cause catchment depressions to retain floodwater. The ultimate goal of river correction is to obtain a river surface elevation that represents the high-water season or typical flood conditions, in order to realistically simulate river drainage during flood season. Furthermore, rivers with dikes only breach when the water level exceeds the dike height; urban rivers typically lack dikes, and rising water levels directly overflow, causing flooding. In this application embodiment, based on the target urban area, the correction should be based on the assumption of no dikes and natural water level fluctuations. To facilitate understanding of the inventive concept of this application, the river research area is selected as the southern moat section of Beijing, referring to... Figure 7 Part (a) shows a schematic diagram of the flooding situation in the DEM of the target urban area.

[0056] Specifically, river elevation correction first requires distinguishing the topographic features of the dry and wet seasons. During the dry season, the river surface elevation is low, the riverbed is exposed, and the surrounding pixel values ​​may be higher than the river surface, causing the DEM to fail to accurately reflect the drainage capacity. Figure 7 Part (b) shows the uncorrected flooding situation of the river surface. At a rainfall rate of 50 mm / h, the flooded elevation of the river reaches 0.83 m, while there is basically no flooding on the banks. During the high-water season, the river surface rises, and the water level approaches or exceeds the riverbank, which is the baseline state for urban flooding simulation. The selected Beijing river DEM has passed the high-water season, so the original DEM data shows that the river surface elevation is lower than the riverbank elevation. During correction, the river surface vector surface is converted into a line, and the equidistant points on the line are assigned the bank elevation to best approximate the river surface state during flooding. Among them, the calculation of the flooding threshold needs to be based on the riverbank height or embankment height. It is assumed that when the water level during the high-water season is 15 m, the river water begins to overflow the riverbank, forming the flooding starting point. This threshold (15 m) is used to define the "red zone"—that is, the flooding starting area, such as riverbank depressions or park lakes. For example, if a large lake exists along a river and its elevation is low during the high-water season, the risk of flooding in the area will be significantly reduced because the lake can absorb more runoff. The larger the lake and the lower its water level, the stronger its flood retention effect. Therefore, the purpose of refining the catchment area is to accurately capture these low points, avoid overestimating the flood depth, ensure that the flood simulation starts from the real critical state, and thus improve the reliability of the results.

[0057] Therefore, when simulating urban flooding in river areas, the river surface vector area data is converted into vector line data using a vector data conversion tool in Geographic Information System (GIS) software. This ensures that the converted vector line data accurately represents the shape and direction of the original river surface. On the vector line data, corresponding sampling points are generated uniformly at preset equidistant intervals. These preset intervals are set according to the actual conditions and accuracy requirements of the study area; for example, one sampling point can be generated every 50 meters to ensure that the sampling points fully reflect changes in river surface elevation.

[0058] The nearest shore point to each sampling point is determined using spatial interpolation algorithms (such as inverse distance weighted interpolation or Kriging interpolation), and the DEM elevation value of the nearest shore point (referred to as the third DEM elevation value) is determined from the low-resolution DEM data. Finally, based on the third DEM elevation value and the location information of the sampling points, the river elevation data is obtained.

[0059] Furthermore, in ArcGIS software, terrain feature points are set at 10-meter intervals along the boundaries of various terrain features, and corresponding elevations are assigned to each point through interpolation. Then, the TIN creation tool is used to construct a preliminary terrain framework. Finally, using ArcGIS's DEM generation tool and a raster interpolation method, a DEM of the small study area is constructed. By constructing a Digital Elevation Model (DEM), the imagery of terrain's influence on flood depth can be accurately quantified. Figure 8 Part (a) is a schematic diagram of the corrected DEM for areas without rivers, showing the road elevations precisely. Figure 8 Part (b) is a modified DEM diagram of areas with rivers and lakes, which raises the elevation of the river and lake surfaces to avoid situations where the flood level in the catchment area is much higher than that on the shore.

[0060] S15 calculates the flood depth based on road elevation data and river elevation data.

[0061] Flooding analysis is conducted based on the presence or absence of rivers in the target urban area. For areas with rivers, the direction of water flow can be determined by the flow direction; generally, floodwater will flow into the river and be discharged along with the river. For areas without rivers, drainage is typically achieved through underground pipes in the lowest-lying area. To simulate flooding scenarios, this embodiment assumes that rainfall is heavy and the drainage capacity of rivers and underground pipes is negligible, leading to a rise in river levels in areas with rivers, causing water to overflow the riverbanks and initiating flooding; in areas without rivers, drainage pipes are blocked, causing water to converge in the lowest-lying area and initiating flooding.

[0062] In an optional implementation, the land cover type data and rainfall parameters are obtained based on the high-resolution DEM data; the land cover type data includes land area and corresponding surface runoff coefficient, and the rainfall parameters include rainfall intensity and corresponding duration. Calculate the effective runoff area based on the surface area and the surface runoff coefficient; Calculate the rainfall depth based on the rainfall intensity and duration; Calculate the runoff volume based on the effective runoff area and the rainfall depth; The flood depth is iteratively calculated based on the runoff volume to generate a flood depth raster map, where each pixel value represents the flood depth of that pixel.

[0063] Once the corrected high-resolution DEM data is acquired, the electronic equipment can obtain rainfall parameters, including rainfall intensity (R, in mm / h) and duration (T, in hours), as well as land cover type data and assign corresponding surface runoff coefficients. Among them, the surface runoff coefficient This is a key hydrological parameter in urban flooding analysis, defined as the ratio of rainfall generated by surface runoff to total rainfall, directly reflecting the efficiency of rainfall conversion into surface runoff. The coefficient ranges from 0 to 1, and its value is primarily controlled by the permeability of the surface cover. In this embodiment, the value is assumed to be 0.90 for impermeable surfaces (roads, plazas, etc.), while the value for grassland is 0.20, reflecting the significant differences in runoff generation capacity among different underlying surfaces.

[0064] To calculate flood depth, electronic devices can construct a flood depth simulation model based on a volume matching algorithm. By iteratively matching the runoff generated by rainfall with the volume of the land represented by the DEM, the distribution of flood depth under different rainfall scenarios can be calculated.

[0065] First, the electronic device uses an area-weighted method to calculate the effective runoff area. :

[0066] in, It is the surface area. This is the surface runoff coefficient.

[0067] Simultaneously, calculate rainfall depth D (Unit: millimeters):

[0068] Among them, R It is the rainfall intensity (unit: mm / hour). T It is the duration (unit: hours).

[0069] Next, based on the effective runoff area A and rainfall depth D Calculate runoff volume V (Unit: cubic meters):

[0070] In obtaining runoff volume V Then, an iterative algorithm was used, starting from the lowest point of the DEM, with the water surface elevation gradually increased in steps of 0.01 meters. The "Surface Volume" tool was used to calculate the inundation volume corresponding to each water level until the error between the calculated inundation volume and the target runoff volume was less than 5%. The simulation results of the flood depth were then visualized in the form of a raster map. Each pixel in the flood depth raster map represents the flood depth at that point. The specific steps of the iterative calculation of flood depth are as follows: (1) Initialization parameters: Set the initial value of water surface elevation. Elevation of the lowest point in the DEM data; (2) Iterative process: a. Gradually increase the water surface elevation in increments of 0.01m. ; b. Use a 3D terrain analysis tool (such as ArcGIS Surface Volume) to calculate the inundation volume at the current water surface elevation. ; c. Calculate the volume error Δ V :

[0071] d. If Δ V If the elevation is ≤5%, stop the iteration and output the current water surface elevation. As the critical value for flooding.

[0072] For each pixel, calculate the flood depth. ; if This indicates that the pixel is submerged at a depth of [value missing]. Otherwise, it is 0.

[0073] In an optional implementation, to verify the improvement effect of DEM refinement and elevation correction on flood simulation, a qualitative analysis is performed by comparing the water flow paths and flood distribution before and after correction. (Refer to...) Figure 9 As shown, the changes in flow direction in areas with and without rivers before and after DEM processing are illustrated, intuitively reflecting the improvement in terrain representation accuracy achieved by the model. (Refer to...) Figure 9 Part (a) and Figure 9Part (b) shows the flow direction map before correction for areas without rivers, and the flow direction map after correction for areas without rivers. In areas without rivers, the original low-resolution DEM (30 meters) cannot accurately capture the low-lying characteristics of roads because it contains multiple land features such as roads and buildings within the pixels. Flow direction map before correction ( Figure 9 In Part (a), the area marked with a black box shows a dispersed water flow path that fails to converge along the road network, deviating from the actual water catchment pattern. Through road elevation correction, the refined DEM (1m resolution) significantly improves the rationality of the flow direction, clearly showing that the water flow forms a continuous catchment path along the lowest points of the road. Figure 9 (Part (b)). This improvement is consistent with subsequent flooding simulation results ( Figure 10 The high degree of agreement demonstrates that the revised DEM more accurately reflects the initiation and expansion process of urbanized areas. (Refer to...) Figure 9 Part (c) and Figure 9 In section (d), the former is the flow direction map before correction for river areas, and the latter is the flow direction map after correction for river areas. In river areas, the river surface elevation of the original low-resolution DEM uses data from the non-high-water season, which is lower than the actual flooding threshold, causing water to flow towards the riverbed but failing to simulate the overflow process. Figure 9 (Part c)). By correcting the river surface elevation to the high-water level, the corrected flow direction diagram ( Figure 9 The middle (d) section shows the water flow direction from land to river, reflecting the natural overflow of rainwater along the riverbank. This effect further confirms the necessity of river elevation correction in urban flooding simulation, such as... Figure 7 In section (b), there was no standing water on the riverbank before the correction, but after the correction, the flooding gradually spread from the starting point of the riverbank. Figure 11 This model aligns with real-world hydrological behavior. To facilitate understanding of the inventive concept, a riverless area in Dongcheng District was selected. The roads in this area have been adjusted to represent the lowest points, and the overall elevation slopes from east to west. Buildings far from roads are flooded using the flood depth of the area covered by the building vector layer, while roadside buildings are flooded using the flood depth of the adjacent road. Since historically, maximum hourly rainfall in Beijing typically lasts for one hour, the rainfall duration is specified as 1 hour in the model to simulate the flooding variations in this riverless area. Figure 10 In section (a), when the rainfall rate R=10, the drainage in several of the lowest zones of the study area was insufficient to form point-like flooding areas, and the flooding did not extend to the entire road. For example... Figure 10 In section (b), when the rainfall rate R=20, rainwater begins to spread across the entire eastern road, with the deepest floodwater reaching 0.25m, potentially affecting residents' travel. Figure 10In section (c), when the rainfall rate R=50, the deepest floodwater on the road reached 0.56m, severely impacting the travel of residents along the eastern side of the road and beginning to spread to higher elevation areas on the west side. For example... Figure 10 In section (d), when the rainfall rate R=100, the deepest floodwater on the road reached 0.79m, which has already seriously affected traffic, and rainwater may have already flooded the first floor of the residential buildings next to the road. Figure 10 In section (e), when the rainfall rate R=200, the deepest flooded level on the road reaches 1.03m, submerging the entire road network in the area. For areas with rivers, the river surface is the lowest elevation point; therefore, rainwater from the land flows into the river, meaning the flooding begins at the river level and spreads towards the banks. The southern moat section of Beijing was selected as the study area, referring to... Figure 7 In section (a), a river and a lake within a park serve as a typical case study. A flood depth simulation model is used to simulate changes in flooding conditions in this area. For example... Figure 11 In part (a), when the rainfall rate R=10, the riverbanks are not yet flooded, and the river level rises by 0.11m. (For example...) Figure 11 In section (b), when the rainfall rate R=20, water quickly overflows the riverbanks, but initially flows mainly into lakes, and the road surface is not flooded. Figure 11 In section (c), when the rainfall rate R=50, rainwater gradually floods the roads, and the water accumulation in the lake raises the lake's surface elevation by approximately 0.2m. For example... Figure 11 In section (d), when the rainfall rate R=100, the water level in the lake rises continuously, flooding the surrounding roads, and the roads on the east side also begin to flood. In section (e), when the rainfall rate R=200, rainwater floods the entire road network of the area. Therefore, the flooding situation differs between terrains near and away from rivers. For areas without rivers, such as some cities, drainage mainly relies on underground pipes. When the underground drainage pipes in the lowest areas become blocked, flooding begins in low-lying areas and gradually spreads to the roads (e.g.,...). Figure 9 Ultimately, the flooding affected low-lying areas and buildings along roads. In areas near riverbanks or large ditches, drainage primarily relies on channeling rainwater into the river or ditch. When the ditches cannot drain the water adequately, the river level gradually rises and overflows the banks. Only when the water level is higher than the riverbank will rainwater flood roads (such as...). Figure 10 It affected the buildings on the shore.

[0074] In other embodiments, since the river surface vector and the bridge surface vector overlap on a two-dimensional plane, the situation can be discussed separately depending on the requirements of the refined DEM: if the goal is to study the depth of ground flooding, then... The layering order from bottom to top, with the river layer covering the bridge layer, ensures that drainage is not obstructed by the bridge surface. (Refer to...) Figure 10Part (a). If the study is to investigate flood relief, then... The stacking order from bottom to top. (Refer to...) Figure 12 When the rainfall is the same Figure 10 In section (a), the flooded area in the diagram does not cover the bridge, so the bridge layer can be represented in the flooded model and used to determine whether to select it as a rescue route.

[0075] Using the aforementioned optional implementation methods, and based on the corrected high-precision DEM, a volume matching algorithm was employed to simulate flooding depths under different rainfall scenarios. The simulation results clearly reveal the significant differences in flooding mechanisms between two typical urban terrains with and without rivers. For areas without rivers (taking Dongcheng District as an example), flooding begins at the lowest point in the area (usually the corrected road intersection or depression), with a "point-to-area" diffusion flooding pattern. When the rainfall intensity (R) is 10 mm / hour, flooding only occurs at local low points such as intersections; when R increases to 50 mm / hour, the flooded area rapidly expands to the entire road network, affecting low-rise buildings and traffic; when R reaches 200 mm / hour, the flooded area reaches a depth of 1.03 meters, posing a serious safety threat. This fully reproduces the flooding process caused by the saturation of sewer drainage capacity in urbanized areas. For areas with rivers, the flooding pattern is a "line-to-area" overflow flooding. In the initial stage of the simulation, precipitation mainly accumulates in the river channel, causing the river level to rise. When R = 10 mm / h, there is no water accumulation on the bank; when R reaches 20 mm / h, the river water begins to overflow the bank, and the water level of the lakes on the bank rises. This indicates that in areas near water bodies, the risk of flooding is directly controlled by the water level of external rivers and the flood retention effect of the catchment areas on the banks, and the flooding threshold is significantly higher than in areas without rivers.

[0076] S16, Flood risk assessment based on the calculated flood depth.

[0077] The flooding depth is divided into five risk levels, represented by different colors: minor flooding, low-risk flooding, medium-risk flooding, relatively high-risk flooding, and high-risk flooding. For example, areas with a flooding depth of less than 15 cm are classified as minor flooding areas; areas with a flooding depth greater than or equal to 15 cm but less than 27 cm are classified as low-risk areas; areas with a flooding depth greater than or equal to 27 cm but less than 40 cm are classified as medium-risk areas; areas with a flooding depth greater than or equal to 40 cm but less than 60 cm are classified as relatively high-risk areas; and areas with a flooding depth greater than or equal to 60 cm are classified as high-risk areas.

[0078] Compared to existing technologies, this application proposes a cost-effective method for converting publicly available 30m resolution digital elevation models (DEMs) into flood models. Focusing on terrain control of the lowest point in the region, it refines and corrects the elevations of roads and rivers in a low-resolution DEM for the first time. By fusing raster (DEM) and vector (road, river) data, it refines and corrects key geographic elements (roads, rivers) influencing flooding behavior in urban flooding. Based on a volume matching algorithm, it achieves rapid simulation of flood depth, establishes a high-resolution flood model, and analyzes the flooding mechanisms in different terrain regions. This solves the problem of applicability in areas with scarce data, meets the needs of resource-constrained areas in the process of urbanization, and provides an easily deployable tool for urban flood control planning. For example, in the case of Dongcheng District in Beijing, it simulates flood risks under different rainfall conditions and can be directly used for emergency management. Through multi-source data fusion and targeted feature correction, this application successfully constructs a low-cost, high-efficiency, and easily deployable urban flood simulation tool, providing a practical risk assessment solution for urban areas with relatively scarce geographic data, promoting the practical application of urban flood models, and laying the foundation for precise and intelligent urban flood control management.

[0079] Reference Figure 13 The diagram shown is a functional block diagram of an urban flooding assessment device according to an embodiment of this application.

[0080] In some embodiments, the urban flooding assessment device 13 may include multiple functional modules composed of computer program segments. The computer programs for each program segment of the urban flooding assessment device 13 may be stored in the memory of an electronic device and executed by at least one processor to perform (see details). Figure 1 (Description) The function of urban flooding assessment. Based on the functions it performs, it can be divided into multiple functional modules. These functional modules may include: an acquisition module 131, a resampling module 132, a road correction module 133, a river correction module 134, and a flood assessment module 135. The module referred to in this application is a series of computer program segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0081] The acquisition module 131 is used to acquire low-resolution DEM data and geographic vector data of the target city area; the geographic vector data includes road vector data and river vector data, and the roads are divided into main roads and non-main roads according to the road type field.

[0082] The resampling module 132 is used to resample the low-resolution DEM data to obtain high-resolution DEM data.

[0083] The road correction module 133 is used to correct the road vector data based on the high-resolution DEM data, so as to perform elevation sampling on the starting and ending points of the main roads and on the starting and ending points of the non-main roads to obtain road elevation data.

[0084] The river correction module 134 is used to correct the elevation of the river vector data to obtain the river elevation data.

[0085] The flood assessment module 135 is used to calculate the flood depth based on road elevation data and river elevation data, and to conduct a flood risk assessment based on the calculated flood depth.

[0086] It should be understood that the various variations and specific embodiments of the urban flooding assessment method provided in the above embodiments are also applicable to the urban flooding assessment device of this embodiment. Through the foregoing detailed description of the urban flooding assessment method, those skilled in the art can clearly understand the implementation method of the urban flooding assessment device of this embodiment. For the sake of brevity, it will not be described in detail here.

[0087] See Figure 14 The diagram shown is a schematic representation of the structure of an electronic device according to an embodiment of this application. In a preferred embodiment of this application, the electronic device 14 includes a memory 141, at least one processor 142, and at least one communication bus 143.

[0088] Those skilled in the art should understand that Figure 14 The structure of the electronic device shown does not constitute a limitation of the embodiments of this application. It can be a bus structure or a star structure. The electronic device 14 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0089] In some embodiments, the electronic device 14 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital processors, and embedded devices. The electronic device 14 may also include user equipment, which includes, but is not limited to, any electronic product capable of human-computer interaction with a user via a keyboard, mouse, remote control, touchpad, or voice control device, such as a personal computer, tablet computer, smartphone, or digital camera.

[0090] In the embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, computer-readable storage media, and electronic devices can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple components or modules may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices, components, or modules may be electrical, mechanical, or other forms.

[0091] The components described as separate parts may or may not be physically separate. The components shown as components may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the components can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each component can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0093] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0096] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for urban waterlogging flood assessment, characterized in that, The method comprises: acquiring low-resolution DEM data and geographic vector data of a target urban area; the geographic vector data comprises road vector data and river vector data, and the roads are divided into main roads and non-main roads according to a road type field; resampling the low-resolution DEM data to obtain high-resolution DEM data; based on the high-resolution DEM data, performing correction processing on the road vector data to perform elevation sampling on the starting points and ending points of the main roads and the starting points and ending points of the non-main roads, and obtaining road elevation data; performing elevation correction on the river vector data to obtain river elevation data; performing flood depth calculation according to the road elevation data and the river elevation data; based on the calculated flood depth, performing flood risk assessment.

2. The urban waterlogging flood assessment method of claim 1, wherein, The correction processing on the road vector data based on the high-resolution DEM data to perform elevation sampling on the starting points and ending points of the main roads and the starting points and ending points of the non-main roads to obtain road elevation data comprises: extracting first DEM elevation values corresponding to the starting point coordinates and the ending point coordinates of the main roads from the low-resolution DEM data; creating a buffer width of the non-main roads according to a road width field, and extracting second DEM elevation values corresponding to the starting point coordinates and the ending point coordinates of the buffer boundaries from the low-resolution DEM data; performing interpolation processing on the sampling points of each road in the main roads according to the first DEM elevation values, and performing interpolation processing on the sampling points of each road in the non-main roads according to the second DEM elevation values to obtain road elevation data.

3. The urban waterlogging flood assessment method of claim 2, wherein, The interpolation processing on the sampling points of each road in the main roads according to the first DEM elevation values comprises: grouping the sampling points of each road in the main roads and the non-main roads according to road ID as a grouping key: determining whether the number of road points in each group is greater than or equal to 2; when the number of road points in each group is less than 2, skipping the interpolation processing and retaining the original sampling points; when the number of road points in each group is greater than or equal to 2, for the main roads, calculating a first distance between the starting point coordinates and the ending point coordinates of the main roads; calculating the elev_interp value of each group of road points in the main roads according to the first DEM elevation values and the first distance through linear interpolation.

4. The urban waterlogging flood assessment method of claim 3, wherein, The interpolation processing on the sampling points of each road in the non-main roads according to the second DEM elevation values comprises: matching the nearest main road point in the main roads through a nearest neighbor algorithm; copying the first DEM elevation value of the nearest main road point as the reference DEM elevation value of each group of road points in the non-main roads; calculating a second distance between the starting point coordinates and the ending point coordinates of the buffer boundaries; calculating the elev_interp value of each group of road points in the non-main roads according to the second DEM elevation values, the reference DEM elevation value and the second distance through linear interpolation.

5. The urban waterlogging flood assessment method of claim 4, wherein, The elev_interp value of each group of road points in the non-main roads is calculated according to the following formula: wherein, elev interp value for each set of road points in the non-major road, start point corresponding second DEM elevation value, end point corresponding second DEM elevation value, and cumulative distance, cumulative distance for current point; The following formula is used to calculate and : wherein, is the most recent major road point, is the reference DEM elevation value; The nearest main road point is determined by the following formula: a set of road points for a primary road to be amended, a set of road points for a non-primary road to be amended, is the second distance.

6. The urban flood inundation assessment method of claim 1, wherein, The elevation correction of the river vector data to obtain the river elevation data includes: Converting the river surface vector surface data in the river vector data into vector line data; On the vector line data, corresponding sampling points are uniformly generated according to a preset equidistance interval; The nearest shore point from each sampling point is determined by a spatial interpolation algorithm, and a third DEM elevation value of the nearest shore point is determined; The river surface elevation is corrected according to the third DEM elevation value to obtain the river elevation data.

7. The urban flood inundation assessment method of claim 1, wherein, The flood depth calculation according to the road elevation data and the river elevation data includes: Obtaining land cover type data and rainfall parameters according to the high-resolution DEM data; the land cover type data includes land area and corresponding land runoff coefficient, and the rainfall parameters include rainfall intensity and corresponding duration; Calculating effective runoff area according to the land area and the land runoff coefficient; Calculating rainfall depth according to the rainfall intensity and the duration; Calculating runoff volume according to the effective runoff area and the rainfall depth; Iterative calculation of flood depth is performed according to the runoff volume to generate a flood depth raster map, and each pixel value represents the flood depth of the pixel.

8. An urban waterlogging inundation assessment device, characterized by, The device includes: An acquisition module is configured to acquire low-resolution DEM data and geographic vector data of a target urban area; the geographic vector data includes road vector data and river vector data, and the roads are divided into main roads and non-main roads according to a road type field; A resampling module is configured to resample the low-resolution DEM data to obtain high-resolution DEM data; A road correction module is configured to correct the road vector data based on the high-resolution DEM data to perform elevation sampling on the starting point and the ending point of the main roads and to perform elevation sampling on the starting point and the ending point of the non-main roads, thereby obtaining road elevation data; A river correction module is configured to correct the river vector data to obtain river elevation data; A flood evaluation module is configured to calculate flood depth according to the road elevation data and the river elevation data, and to perform flood risk evaluation based on the calculated flood depth.

9. An electronic device, comprising: The computer program is executed by the processor to implement the steps of the urban waterlogging flood evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the urban waterlogging flood evaluation method according to any one of claims 1 to 7.

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