Urban flood resilience assessment method, device, equipment, medium and computer program product

Abstract

The application discloses a kind of urban flood resilience assessment method, device, equipment, medium and computer program product, the method includes obtaining the hydro-meteorological data of the region to be evaluated and the geographic information data of city space form element layer;According to the boundary condition of multiple flood scenarios set by hydro-meteorological data, and combined with geographic information data, construct multiple-scenario urban flood disaster simulation model, respectively generate the flood submerged depth data of multiple flood scenarios city space form element layer, calculate the first resilience index, second resilience index, third resilience index of each flood scenario, to calculate the flood resilience comprehensive evaluation result of each flood scenario of the region to be evaluated.The application establishes the assessment system of multiple compound flood disaster scenarios, comprehensively considers different disaster factors and their interaction, fully considers the role of key spatial parameters in urban flood resilience, so that the ability of city to cope with flood disaster can be more comprehensive and accurately evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban flood control simulation evaluation, and in particular to a city flood resilience evaluation method, device, equipment, medium and computer program product. BACKGROUND

[0002] The current city flood resilience evaluation method usually only considers a single flood disaster scenario, does not fully consider multiple composite flood disaster scenarios such as coastal storm surges, river floods, typhoon paths, and heavy rains, and is difficult to meet the evaluation needs of extreme weather events under climate change, and cannot accurately predict and respond to complex and variable flood conditions. Moreover, the current city flood resilience evaluation method does not include various morphological elements of the city in a unified index system, and ignores the influence of key spatial parameters on flood resilience. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a city flood resilience evaluation method, device, equipment, medium and computer program product, establish an evaluation system for multiple composite flood disaster scenarios, comprehensively consider different disaster factors and their interactions, and fully consider the role of key spatial parameters in city flood resilience, so that the ability of the city to respond to flood disasters can be more comprehensively and accurately evaluated.

[0004] To achieve the above purpose, the embodiments of the present application provide a city flood resilience evaluation method, comprising:

[0005] Obtain hydro-meteorological data and geographic information data of a city spatial morphological element layer of a region to be evaluated; wherein the hydro-meteorological data includes water level data, rainfall data, typhoon data and SLR prediction data; the city spatial morphological element layer includes a landscape ecological spatial element layer, a street network spatial element layer and a street block building spatial element layer;

[0006] Set boundary conditions for multiple flood scenarios according to the hydro-meteorological data, and construct a multi-scenario city flood disaster simulation model in combination with the geographic information data of the city spatial morphological element layer; wherein the multi-scenario city flood disaster simulation model includes a river-surface-pipe network coupling model for multiple flood scenarios;

[0007] According to the multi-scenario city flood disaster simulation model, generate flood inundation depth data of the landscape ecological spatial element layer, the street network spatial element layer and the street block building spatial element layer for multiple flood scenarios, respectively;

[0008] According to the flood submergence depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of each of the multiple flood scenarios, a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer, and a third resilience index of the block building space element layer of each of the multiple flood scenarios are calculated.

[0009] According to the first resilience index, the second resilience index, and the third resilience index, a comprehensive flood resilience evaluation result of each of the multiple flood scenarios of the to-be-evaluated region is calculated.

[0010] As an improvement of the above solution, the geographic information data comprises topographic data, urban water body and green space system data, urban street network data, municipal drainage pipe network data, urban land use data, POI data of emergency shelters, and POI data of key service facilities.

[0011] As an improvement of the above solution, the boundary conditions of the multiple flood scenarios are set according to the hydro-meteorological data, and a multi-scenario urban flood disaster simulation model is constructed in combination with the geographic information data of the urban space form element layer, comprising:

[0012] The boundary conditions of the multiple flood scenarios are set according to the water level data, the rainfall data, the typhoon data, and the SLR prediction data; wherein the boundary conditions of the multiple flood scenarios are sequentially increased;

[0013] Based on the boundary conditions of the multiple flood scenarios, a river model of the multiple flood scenarios is constructed in combination with the geographic information data of the urban space form element layer;

[0014] Based on the boundary conditions of the multiple flood scenarios, a surface model of the multiple flood scenarios is constructed in combination with the geographic information data of the urban space form element layer;

[0015] Based on the boundary conditions of the multiple flood scenarios, a pipe network model of the multiple flood scenarios is constructed in combination with the geographic information data of the urban space form element layer;

[0016] The river model, the surface model, and the pipe network model of the multiple flood scenarios are coupled to obtain the multi-scenario urban flood disaster simulation model.

[0017] As an improvement of the above solution, the first resilience index of the landscape ecological space element layer, the second resilience index of the street network space element layer, and the third resilience index of the block building space element layer of each of the multiple flood scenarios are calculated according to the flood submergence depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of each of the multiple flood scenarios, comprising:

[0018] combining the flood inundation depth data of the landscape ecological space element layer of multiple flood scenarios with the urban water body and green space system data, to generate corresponding landscape ecological space inundation distribution data;

[0019] According to the landscape ecological space inundation distribution data, the first resilience index of the landscape ecological space element layer of each flood scenario is calculated;

[0020] combining the flood inundation depth data of the street network space element layer of multiple flood scenarios with the urban street network data and the POI data of emergency shelters, to generate corresponding street network space inundation distribution data;

[0021] According to the street network space inundation distribution data, the second resilience index of the street network space element layer of each flood scenario is calculated;

[0022] combining the flood inundation depth data of the block building space element layer of multiple flood scenarios with the urban street network data, the urban land use data and the POI data of key service facilities, to generate corresponding block building space inundation distribution data;

[0023] According to the block building space inundation distribution data, the third resilience index of the block building space element layer of each flood scenario is calculated.

[0024] As an improvement of the above scheme, the first resilience index includes landscape ecological space and inundation range overlap degree, flood peak reduction rate and flood peak delay time;

[0025] The second resilience index includes in-disaster street network walkability, in-disaster emergency shelter distribution density, in-disaster emergency shelter walkability and inundated street network disaster damage rate;

[0026] The third resilience index includes post-disaster key service facility distribution density, post-disaster key service facility walkability and inundated land disaster damage rate.

[0027] As an improvement of the above scheme, according to the first resilience index, the second resilience index and the third resilience index, the flood resilience comprehensive evaluation result of each flood scenario of the to-be-evaluated region is calculated, including:

[0028] The first resilience index, the second resilience index and the third resilience index of each flood scenario are standardized;

[0029] The first, second and third resilience indexes are weighted and summed to obtain a flood resilience comprehensive evaluation result of each of the flood scenarios of the to-be-evaluated region.

[0030] The embodiment of the present application also provides a city flood resilience evaluation device, which comprises:

[0031] A data acquisition module is configured to acquire hydro-meteorological data and geographic information data of a city spatial form element layer of a to-be-evaluated region, wherein the hydro-meteorological data comprises water level data, rainfall data, typhoon data and SLR prediction data, and the city spatial form element layer comprises a landscape ecological space element layer, a street network space element layer and a block building space element layer.

[0032] A model construction module is configured to set boundary conditions of a plurality of flood scenarios according to the hydro-meteorological data, and construct a multi-scenario city flood disaster simulation model in combination with the geographic information data of the city spatial form element layer, wherein the multi-scenario city flood disaster simulation model comprises a plurality of river-ground-pipe network coupling models of the flood scenarios.

[0033] A flood simulation module is configured to generate flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of each of the flood scenarios according to the multi-scenario city flood disaster simulation model.

[0034] An index calculation module is configured to calculate a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer and a third resilience index of the block building space element layer of each of the flood scenarios according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of each of the flood scenarios.

[0035] A comprehensive evaluation module is configured to calculate a flood resilience comprehensive evaluation result of each of the flood scenarios of the to-be-evaluated region according to the first, second and third resilience indexes.

[0036] The embodiment of the present application also provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the city flood resilience evaluation method when executing the computer program.

[0037] The embodiment of the present application also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program controls a device where the computer readable storage medium is located to perform the urban flood resilience assessment method when the computer program runs.

[0038] The embodiment of the present application also provides a computer program product, which comprises a computer program or computer instructions, and the computer program or the computer instructions realize the urban flood resilience assessment method when executed by a processor.

[0039] Compared with the prior art, the urban flood resilience assessment method, device, equipment, medium and computer program product provided by the embodiment of the present application have the beneficial effects that: hydro-meteorological data of a to-be-evaluated region and geographic information data of a city spatial form element layer are acquired; the hydro-meteorological data comprises water level data, rainfall data, typhoon data and SLR prediction data; the city spatial form element layer comprises a landscape ecological space element layer, a street network space element layer and a block building space element layer; boundary conditions of multiple flood scenarios are set according to the hydro-meteorological data, and a multi-scenario urban flood disaster simulation model is constructed in combination with the geographic information data of the city spatial form element layer; the multi-scenario urban flood disaster simulation model comprises river-ground-pipe network coupling models of multiple flood scenarios; flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios are respectively generated according to the multi-scenario urban flood disaster simulation model; a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer and a third resilience index of the block building space element layer of each flood scenario are calculated according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios; and a flood resilience comprehensive evaluation result of each flood scenario of the to-be-evaluated region is calculated according to the first resilience index, the second resilience index and the third resilience index. The embodiment of the present application establishes an evaluation system of multiple compound flood disaster scenarios, comprehensively considers different disaster factors and their interactions, fully considers the role of key spatial parameters in urban flood resilience, and thus can more comprehensively and accurately evaluate the ability of a city to cope with flood disasters. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a flowchart of a preferred embodiment of an urban flood resilience assessment method provided by the present application;

[0041] Figure 2It is a street network element layer flood disaster coupling analysis schematic diagram in a city flood resilience assessment method provided by the application.

[0042] Figure 3 It is a structural schematic diagram of a preferred embodiment of a city flood resilience assessment device provided by the application.

[0043] Figure 4 It is a structural schematic diagram of a preferred embodiment of a terminal device provided by the application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the application.

[0045] Please refer to Figure 1 , Figure 1 It is a flow schematic diagram of a preferred embodiment of a city flood resilience assessment method provided by the application. The city flood resilience assessment method comprises:

[0046] S1, acquiring hydro-meteorological data of a region to be evaluated and geographic information data of a city spatial form element layer; wherein the hydro-meteorological data comprises water level data, rainfall data, typhoon data and SLR prediction data; and the city spatial form element layer comprises a landscape ecological space element layer, a street network space element layer and a block building space element layer;

[0047] S2, setting boundary conditions of multiple flood scenarios according to the hydro-meteorological data, and combining the geographic information data of the city spatial form element layer, constructing a multi-scenario city flood disaster simulation model; wherein the multi-scenario city flood disaster simulation model comprises a river-surface-pipe network coupling model of multiple flood scenarios;

[0048] S3, generating flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios respectively according to the multi-scenario city flood disaster simulation model;

[0049] S4, calculating a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer and a third resilience index of the block building space element layer of each flood scenario according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios.

[0050] S5, calculating a flood resilience comprehensive evaluation result of each of the flood scenarios of the to-be-evaluated region according to the first resilience index, the second resilience index, and the third resilience index.

[0051] Specifically, the embodiment of the present application first acquires hydro-meteorological data of a to-be-evaluated region and geographic information data of a city spatial form element layer. The hydro-meteorological data includes water level data, rainfall data, typhoon data, and SLR prediction data. The city spatial form element layer includes a landscape ecological space element layer, a street network space element layer, and a block building space element layer. Then, boundary conditions of multiple flood scenarios are set according to the hydro-meteorological data, and a multi-scenario city flood disaster simulation model is constructed in combination with the geographic information data of the city spatial form element layer. The multi-scenario city flood disaster simulation model includes a river-surface-pipe network coupling model of multiple flood scenarios. Exemplarily, the multi-scenario city flood disaster simulation model can be realized by coupling a Mike 11 one-dimensional river module, a Mike 21 two-dimensional surface overland flow module, and a MikeUrban pipe network module in the MikeFlood software. Secondly, according to the multi-scenario city flood disaster simulation model, a result grid file of flood inundation simulation of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of each of the multiple flood scenarios is generated, and geographic information spatialization processing is performed to obtain flood inundation depth data. Thirdly, according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of each of the multiple flood scenarios, a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer, and a third resilience index of the block building space element layer of each of the flood scenarios are calculated. Finally, a flood resilience comprehensive evaluation result of each of the flood scenarios of the to-be-evaluated region is calculated according to the first resilience index, the second resilience index, and the third resilience index.

[0052] The embodiment of the present application establishes an evaluation system for multiple composite flood disaster scenarios, comprehensively considers different disaster factors and their interactions, fully considers the role of key spatial parameters in city flood resilience, and thus can more comprehensively and accurately evaluate the ability of a city to cope with flood disasters, accurately evaluate the flood risk faced by the city under the background of climate change and the frequent occurrence of extreme weather events, and provide comprehensive and effective decision support for the city to cope with complex and variable flood disasters.

[0053] In another preferred embodiment, the geographic information data includes terrain data, city water body and green space system data, city street network data, municipal drainage pipe network data, city land use data, POI data of emergency shelters, and POI data of key service facilities.

[0054] Specifically, the geographic information data in the embodiments of the present application includes terrain data, urban water body and green space system data, urban street network data, municipal drainage pipe network data, urban land use data, POI data of emergency shelter sites, and POI data of key service facilities.

[0055] The terrain data includes land elevation, seabed elevation, and riverbed elevation. The land elevation (DEM) is obtained from NASA SRTM or LiDAR; the seabed elevation data is obtained from GEBCO or a digital chart; and the riverbed elevation is obtained from measurement data of a hydrological monitoring station. The above multi-source elevation data is integrated into a unified elevation map in ARCGIS. For example, the integration steps are as follows:

[0056] Step one: elevation datum conversion and data cleaning. Identify the data format and resolution of land, seabed, and riverbed. The land elevation adopts EGM2008 geoid, and the seabed adopts the lowest astronomical tide level, and the elevation datum conversion needs to be performed through a vertical offset model. The DEM data is smoothed to remove noise and outliers, and is filled to avoid unreasonable water accumulation in the simulation process.

[0057] Step two: unified spatial reference system. Convert all data to UTM projection, and realize spatial alignment of multi-source data through geographic registration and geometric correction.

[0058] Step three: multi-source data fusion. Use Kriging interpolation to generate DEM grid for elevation point data; perform mask processing on the elevation grid of land, seabed, and riverbed according to their distribution range, and then use the MosaicToNewRaster toolbox of ARCGIS to splice the elevation grids; use the GDAL library of Python to perform spatial resolution resampling on the spliced elevation grid to obtain terrain GeoTIFF data file with a precision of 5m.

[0059] The urban water body and green space system data includes water network, water surface distribution and morphology, green space distribution and morphology, and the like. The above water body and green space system data is obtained by vectorization in ARCGIS according to the land use status of the research area, Google Earth satellite images, and the like.

[0060] The urban street network data is obtained by vectorization from OpenStreetMap (OSM) or according to the land use status of the research area, planning and design scheme.

[0061] The municipal drainage pipe network data includes the position, diameter, and buried depth of manholes, and the position, diameter, and slope of the pipe network. The acquisition approaches include: 1) applying to the municipal management department or the urban planning department to obtain the pipe network data in the study area; 2) referring to relevant academic papers and research reports; and 3) when the municipal drainage pipe network data is missing, referring to the “Outdoor Drainage Design Standard (GB50014-2021)” to vectorize the municipal drainage pipe network data, so as to ensure that the layout of the drainage pipe network matches the trend of the urban road.

[0062] The urban land use data is obtained from the urban planning department and the government public data platform.

[0063] The POI data of the emergency shelter and the POI data of the key service facilities are obtained from the map open platform based on Python crawling.

[0064] In another preferred embodiment, S2, according to the hydro-meteorological data, sets boundary conditions of multiple flood scenarios, and combines the geographic information data of the urban spatial form element layer to construct a multi-scenario urban flood disaster simulation model, including:

[0065] S21, according to the water level data, the rainfall data, the typhoon data, and the SLR prediction data, sets boundary conditions of multiple flood scenarios; wherein the boundary conditions of the multiple flood scenarios are sequentially increased;

[0066] S22, based on the boundary conditions of the multiple flood scenarios, combines the geographic information data of the urban spatial form element layer to construct a river model of multiple flood scenarios;

[0067] S23, based on the boundary conditions of the multiple flood scenarios, combines the geographic information data of the urban spatial form element layer to construct a surface model of multiple flood scenarios;

[0068] S24, based on the boundary conditions of the multiple flood scenarios, combines the geographic information data of the urban spatial form element layer to construct a pipe network model of multiple flood scenarios;

[0069] S25, couples the river model, the surface model, and the pipe network model of the multiple flood scenarios to obtain the multi-scenario urban flood disaster simulation model.

[0070] Specifically, in the process of setting multiple flood scenarios boundary conditions according to hydro-meteorological data, the embodiment of the present application first identifies key water level meteorological factors affecting historical flood events, obtains time series data of these historical events including water level, rainfall, typhoon, etc. from the hydrological monitoring and meteorological department where the to-be-evaluated area is located, to decompose flood, tide, and waterlogging disaster factors. Then, the future predicted sea level rise (SLR) vertical data is obtained to quantify the impact of long-term climate change on flood risk. Specifically, the flood disaster factors mainly include long-time and high-intensity rainfall in the basin and upstream inflow; the tide disaster factors involve tidal cycle water level change, typhoon storm surge, and water level rise caused by sea level rise; and the waterlogging disaster factors are related to short-time heavy rainfall and water level accumulation. When setting the time series of the boundary conditions, the parameters of the key disaster factors are gradually increased according to the preset scenarios. For example, the water level and rainfall intensity are set according to the historical data of the disaster return period, the typhoon wind field is set according to the storm intensity, and the SLR value is set according to the predicted values of 2050 and 2100, respectively. Taking six flood disaster scenarios of an area as an example, taking the historical extreme typhoon event “Tian'ge” scenario 4 as a boundary, scenarios 1-4 are historical flood disaster scenarios that have occurred, and scenarios 5-6 are disaster scenarios of multiple types of historical extreme disaster factors superimposed with future predictions, as shown in Table 1 below.

[0071] Table 1 Boundary conditions of six flood scenarios

[0072]

[0073] Among them, the water level data is obtained by querying the monitoring data of the local water conservancy department, consulting relevant scientific research papers and other materials; the rainfall intensity data is determined according to the design storm return period, rainfall duration and other storm parameters of the relevant specifications of the research area, and the rainfall intensity of each period is obtained by selecting the rainfall distribution proportion of the local typical rain type; the typhoon data is obtained from the typhoon monitoring information released by the meteorological department, including typhoon position, wind speed, wind direction, air pressure and other key parameters; the SLR prediction data is obtained according to the IPCC AR6 high emission scenario, and the predicted SLR is about 50 cm by 2050 and 100 cm by 2100.

[0074] Based on the boundary conditions of multiple flood scenarios, combined with the geographic information data of the urban spatial form element layer, a river model of multiple flood scenarios is constructed. For example, it is realized by the Mike 11 one-dimensional river module in the MikeFlood software, and the construction steps are as follows:

[0075] Step 1: According to the water system data in the urban water and green space system data, draw the river center line in the order of upstream to downstream, generate the river network file in the research range, input it into the “.nwk11” file of Mike11 and set the connection relationship between the rivers;

[0076] Step two: According to the terrain GeoTIFF data with the precision of 5m, generate the river section line perpendicular to the river center line every 50m along the river center line drawn in step one. Use the "3DAnalyst" tool of ArcGIS to draw the section line from the left bank to the right bank according to the section line by using the line insertion tool, and get the section profile drawing. After generating the profile drawing, export the txt file and input it into the ".xns11" file of Mike11;

[0077] Step three: According to the water level data in the hydro-meteorological data, form the time series file of the downstream boundary of the river under the flood scenarios 1~6, and input it into the ".bnd11" file of Mike11;

[0078] Step four: Run the Mike11 module.

[0079] Based on the boundary conditions of multiple flood scenarios, combined with the geographic information data of the urban spatial form element layer, the surface model of multiple flood scenarios is constructed. For example, it is realized by using the Mike21 two-dimensional surface flow module in the MikeFlood software, and the construction steps are as follows:

[0080] Step one: Use the "RastertoASCII" toolbox in ArcGIS to convert the terrain GeoTIFF data file with the precision of 5m into terrain ASCII data file. Convert the terrain ASCII data into elevation surface file (.dfs2 format) by using the Grd2Mike tool of Mike Zero Toolbox, and input it into Mike21 "Bathymetry";

[0081] Step two: Based on the urban land use data, according to the corresponding Manning's M data of different land use types, as shown in the following table 2. Generate the Manning's M ASCII data with the same pixel size, location, range and terrain ASCII data by using the polygontoraster and rastertoASCII toolboxes of ArcGIS; Convert the Manning's M ASCII data into roughness surface file (.dfs2 format) by using the Grd2Mike tool of Mike Zero Toolbox, and input it into Mike21 "Resistance";

[0082] Table 2 Manning's M corresponding to different land use types

[0083]

[0084] Step three: According to the water level data in the hydro-meteorological data, generate historical tidal level, historical tidal level superimposed on SLR predicted tidal level, form the opening boundary time series file of flood scenarios 1-6, input into Mike21 "Boundary";

[0085] Step four: According to the wind speed and direction of typhoon of different intensity levels in flood scenarios 1-6, generate typhoon wind field file, input into Mike21 "WindCondition";

[0086] Step five: Run Mike21 module.

[0087] Based on the boundary conditions of multiple flood scenarios, combined with the geographic information data of the urban spatial form element layer, the pipe network model of multiple flood scenarios is constructed. For example, it is realized by MikeUrban pipe network module in MikeFlood software, and the construction steps are as follows:

[0088] Step one: Import the position, diameter, buried depth of manhole in municipal drainage pipe network data, and the attribute information of pipe network position, diameter, slope, etc., and the research area range (. shp format) into MikeUrban;

[0089] Step two: Use "CatchmentDelineationWizard" and "CatchmentConnectionWizard" tools in MikeUrban in turn to divide the catchment area of the study area, and connect the manhole with the catchment area;

[0090] Step three: Import different land use type surface files (. shp format) into "CatchmentProcessing" of MikeUrban, and then input the impermeable rate parameters of different land use types according to "Outdoor Drainage Design Standard ( GB50014-2021) ", as shown in the following table 3;

[0091] Table 3 Impermeable rate of different land use types

[0092]

[0093] Step four: Set the model boundary conditions according to the rainfall intensity time series file (. dfs0 format) in the hydro-meteorological data;

[0094] Step five: Set runoff model and pipe flow model respectively, and run MikeUrban module.

[0095] Couple the riverway model, the surface model and the pipe network model of multiple flood scenarios to obtain a multi-scenario urban flood disaster simulation model.

[0096] Post-process the result file (.dfs2 format) generated by the multi-scenario urban flood disaster simulation model by means of the Mike2Grd tool of the MikeZeroToolbox to generate a two-dimensional flood simulation submerged water depth result grid file with a pixel size of 5 m, so as to be integrated into the ArcGIS platform for geographic information spatialization processing subsequently.

[0097] The embodiment of the present application constructs a model system capable of effectively realizing three-dimensional dynamic coupling of riverways, surfaces and pipe networks, more accurately simulates the propagation process of floods in different spaces (riverways, surfaces and pipe networks) in cities by enhancing the coupling degree between different modules, thereby effectively reducing simulation errors and providing a more reliable basis for prediction and prevention of flood disasters.

[0098] In another preferred embodiment, the S4 calculates the first resilience index of the landscape ecological space element layer, the second resilience index of the street network space element layer and the third resilience index of the block building space element layer of each flood scenario according to the flood submerged depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios, including:

[0099] S41, combine the flood submerged depth data of the landscape ecological space element layer of multiple flood scenarios with the urban water body and green land system data to generate corresponding landscape ecological space submerged distribution data;

[0100] S42, calculate the first resilience index of the landscape ecological space element layer of each flood scenario according to the landscape ecological space submerged distribution data;

[0101] S43, combine the flood inundation depth data of the street network spatial element layer of multiple flood scenarios with the urban street network data and the POI data of the emergency shelter, to generate corresponding street network spatial inundation distribution data;

[0102] S44, calculate the second resilience index of the street network spatial element layer of each flood scenario according to the street network spatial inundation distribution data;

[0103] S45, combine the flood inundation depth data of the block building spatial element layer of multiple flood scenarios with the urban street network data, the urban land use data and the POI data of the key service facilities, to generate corresponding block building spatial inundation distribution data;

[0104] S46, calculate the third resilience index of the block building spatial element layer of each flood scenario according to the block building spatial inundation distribution data.

[0105] Specifically, according to the multi-scenario urban flood disaster simulation model, the present application generates multiple sets of 48-time 5m-precision flood inundation depth raster files (the raster pixel value is the inundation depth h), combines the vector surface files (.shp format) of the urban water body and green space system, generates a landscape ecological spatial distribution raster file with the same precision and location as the inundation depth raster file, obtains the landscape ecological spatial inundation distribution data, calculates the first resilience index of the landscape ecological spatial element layer of each flood scenario, and obtains the time sequence data of the first resilience index of the landscape ecological spatial element layer of each flood scenario.

[0106] According to the multi-scenario urban flood disaster simulation model, the present application generates multiple sets of 48-time 5m-precision flood inundation depth raster files (the raster pixel value is the inundation depth ), combines the vector line files (.shp format) of the urban street network and the POI point files (.shp format) of the emergency shelter. Please refer to Figure 2 , Figure 2 is a schematic diagram of street network element layer flood disaster coupling analysis in a city flood resilience evaluation method provided by the present application. When the street network path or the emergency shelter point is in the ≥0.15m grid range, it is considered to be inundated. Remove the inundated path of the street network and the emergency shelter point, generate the street network line file and the emergency shelter point file that are not inundated in the disaster, obtain the street network spatial inundation distribution data, calculate the second resilience index of the street network spatial element layer of each flood scenario, and obtain the time sequence data of the second resilience index of the street network spatial element layer of each flood scenario.

[0107] Based on a multi-scenario urban flood disaster simulation model, multiple sets of 5m precision flood inundation depth raster files were generated at 48 time points (raster cell values ​​represent inundation depth). This combines vector line files (.shp format) of the urban street network, vector polygon files (.shp format) of urban land use, and POI point files (.shp format) of key service facilities. When street network paths, land use, and service facility points are located... A grid area ≥0.15m is considered flooded. Flooded paths and service facility points in the street network are removed, generating files for street network lines not flooded during the disaster and files for key service facility points after the disaster. The flooded land use areas of various types are vectorized, generating flooded land use surface files. This yields data on the flooded distribution of street building space. The third resilience index of the street building space element layer for each flood scenario is calculated, resulting in time-series data for the third resilience index of the street building space element layer for each flood scenario.

[0108] In yet another preferred embodiment, the first resilience index includes the overlap between the landscape ecological space and the inundation area, the flood peak reduction rate, and the flood peak delay time;

[0109] The second resilience index includes the pedestrian accessibility of the street network during a disaster, the distribution density of emergency shelters during a disaster, the pedestrian accessibility of emergency shelters during a disaster, and the damage rate of the flooded street network;

[0110] The third resilience index includes the density of critical service facilities after disaster, the walkability of critical service facilities after disaster, and the damage rate of inundated land.

[0111] Specifically, in this embodiment of the invention, the first resilience index of the landscape ecological space element layer includes the overlap between landscape ecological space and inundation area, peak flow reduction rate, and peak flow delay time. Here, the overlap between landscape ecological space and inundation area (OR) is the ratio of the inundation area of ​​urban water bodies and green spaces (including landscape ecological spaces) to the total inundation area for each flood scenario; the peak flow reduction rate (PDR) is the ratio of the maximum instantaneous flow that landscape ecological space can reduce in a flood scenario to the peak flow; and the peak flow delay time (PDT) is the time difference before and after the peak flow is delayed.

[0112] For example, the analysis steps and index calculation formula for the overlap (OR) between landscape ecological space and inundation area are as follows:

[0113] Step 1: Use Python's rasterio library to read flood inundation raster and landscape ecological spatial distribution raster;

[0114] Step 2: Use Python's NumPy library to perform logical operations on the two raster data, and use a Boolean mask to find... The overlap range between the inundation depth raster (>0) and the landscape ecological spatial distribution raster is calculated, and the overlap area is calculated.

[0115] Step 3: Calculate the ratio of the inundated area of ​​the landscape ecological space to the total inundated area using formula (1).

[0116] (1)

[0117] in: For the first Area of ​​each flood-affected grid unit For the first The area of ​​overlapping grid units in blue-green landscape ecological spaces and flood-prone areas. The total number of grid cells in the flooded area. This represents the total number of grid units that overlap with blue-green landscape ecological spaces and flood-prone areas.

[0118] For example, the analysis steps and calculation formula for peak reduction rate (PDR) are as follows:

[0119] Step 1: Use rasterio to read the flooding depth raster files for 48 times of a certain scenario one by one, and use numpy to calculate the flow rate at that time by summing the depth data of the raster cells and multiplying it by the area of ​​the raster cells.

[0120] Step 2: Compare the current flow rate of the raster with the known maximum value, and update the maximum value and the corresponding raster file name to "Flood Peak Inundation Depth Raster File";

[0121] Step 3: Calculate the ratio of the maximum instantaneous flow that can be reduced in the landscape ecological space to the peak flow in the flood scenario using formula (2).

[0122] (2)

[0123] in: For the peak of the flood Area of ​​each flood-affected grid unit For this time the The area of ​​overlapping grid units in blue-green landscape ecological spaces and flood-prone areas. This represents the total number of grid cells in the flooded area at the peak of the flood. This refers to the total number of grid units that overlap between the blue-green landscape ecological space and the flooded area at this time. The flooding depth of the grid cell.

[0124] For example, the analysis steps and calculation formula for peak delay time (PDT) are as follows:

[0125] Step 1: Use rasterio and numpy to generate a raster file of flood peak time and inundation depth, and obtain the corresponding time from the file. ;

[0126] Step 2: Combine the landscape ecological space distribution raster file, use rasterio and numpy to calculate the overlap range between the landscape ecological space and the flooded area at each time point. At this time, calculate the flow rate of the flood inundation depth raster at that time point, excluding the overlap range, using formula (3).

[0127] (3)

[0128] in: For the first Area of ​​each flood-affected grid unit For the first The area of ​​overlapping grid units in blue-green landscape ecological spaces and flood-prone areas. The total number of grid cells in the flooded area. The total number of grid units that overlap with blue-green landscape ecological spaces and flood-prone areas. The flooding depth of the grid cell.

[0129] Step 3: Compare the total flow of the grids at the 48 time points in Step 2, excluding those overlapping with the landscape ecological space. Compared with the known maximum value, obtain The time corresponding to the maximum value ;

[0130] Step 4: Calculate the time difference that the landscape ecological space can delay the flood peak in a flood scenario using formula (4).

[0131] (4)

[0132] The second resilience index of the street network spatial element layer includes the pedestrian accessibility of the street network during a disaster, the distribution density of emergency shelters during a disaster, the pedestrian accessibility of emergency shelters during a disaster, and the disaster loss rate of the submerged street network. Specifically, the pedestrian accessibility (ANS) of the street network during a disaster is the accessibility of the street network within a 500m radius after removing the submerged path at each flood peak in each scenario; the distribution density (DIS) of emergency shelters during a disaster is the kernel density of emergency shelters that were not submerged; the pedestrian accessibility (AIS) of emergency shelters during a disaster is the accessibility of emergency shelters within a 500m walking radius of those that were not submerged; and the disaster loss rate (DRS) of the submerged street network is the ratio of the path loss value caused by flooding to the normal value, taking into account road level.

[0133] For example, the analysis steps and index calculation formulas for the pedestrian accessibility (ANS) of street networks during disasters are as follows:

[0134] Step 1: Run the ARCGIS-based spatial design network analysis plug-in sDNA to prepare the network of the non-flooded street network line file in the disaster (PrepareNetwork);

[0135] Step 2: Run the Integral Analysis of sDNA according to the network prepared in Step 1;

[0136] Step 3: Calculate the pedestrian accessibility of the street network in the flood scenario in sDNA by Formulas (5-6).

[0137] (5)

[0138] (6)

[0139] Wherein: is the set of all nodes within the search radius range of the starting node , and the pedestrian radius is set to 500m; is the shortest topological distance from node to any node ; is the weight of any node ; is the proportion of node within the radius range of node , if it is a discrete spatial analysis, its value is 0 or 1, i.e. is taken as 1 within the radius range, and if it is a continuous spatial analysis, ; and The default value is 1, and if the network quantity and accessibility need to be considered, the coefficients of the multivariate hyperloglinear regression model can be obtained based on the average distance and weight; Take the median of the pedestrian accessibility of each path of the street network .

[0140] For example, the analysis steps and index calculation formulas of the distribution density (DIS) of emergency shelters in the disaster are as follows:

[0141] Step 1: Based on the Python crawler code and the map open platform, obtain the POIs of emergency shelters including community service stations, fire stations, gymnasiums, museums or other art venues in the study area, remove the flooded POIs to form a point file of non-flooded emergency shelters in the disaster;

[0142] Step 2: Use the Kernel Density analysis (KernelDensity) in ARCGIS to calculate the distribution density of emergency shelters per square kilometer by Formulas (7-9).

[0143] (7)

[0144] (8)

[0145] (9)

[0146] wherein: is the kernel density value of position ; is the POI count of the non-flooded emergency shelter; is the bandwidth parameter, which controls the range of the field when calculating the density; is the kernel function, which is used to weight each point, and the selected kernel function is the Gaussian kernel function ; is the coordinate of the th point in the point data set; is the Euclidean distance from position to the th point; is the normalized distance, which is the value obtained by dividing the actual distance by the bandwidth ; takes the median of .

[0147] Exemplarily, the analysis steps and index calculation formula of the walk-up accessibility of emergency shelters in disasters (AIS) are as follows:

[0148] Step one: read the vector data of the emergency shelters in disasters and the street network for walk-up accessibility analysis generated in the analysis steps and index calculation formula of the street network walk-up accessibility in disasters (ANS) using the geopandas library of Python;

[0149] Step two: calculate the distance from each shelter to all streets and find the nearest street using the distance method, and extract the value of the walk-up accessibility field from the nearest street, see formula (10).

[0150] (10)

[0151] wherein: is the walk-up accessibility of the street nearest to a certain non-flooded shelter.

[0152] Exemplarily, the analysis steps and index calculation formula of the disaster damage rate of submerged street network (DRS) are as follows:

[0153] Step one: record the length of the submerged street network path and its road grade (such as trunk road, secondary road, branch road, etc.);

[0154] Step two: Calculate the disaster damage rate of the submerged street network by formula (11).

[0155] (11)

[0156] Wherein: is the road grade index, which divides the street network into 1 to multiple grades according to different grades of roads (such as trunk roads, secondary roads, branch roads, etc.); is the weight assigned to each road grade, as shown in Table 4 below, reflecting the importance of roads of this grade; is the number of paths submerged in the first class road; is the total number of paths in the first class road.

[0157] Table 4: Calculation weight of disaster damage rate of each road grade

[0158]

[0159] The third resilience index of the block building space element layer includes the distribution density of post-disaster key service facilities, the walkability of post-disaster key service facilities, and the disaster damage rate of submerged land. Among them, the distribution density of post-disaster key service facilities DCF is the nuclear density of un-submerged key service facilities at each scenario flood peak time; the walkability of post-disaster key service facilities ACF is the 500m walkability of un-submerged key service facilities; the disaster damage rate of submerged land DRU is the ratio of the loss value of submerged land to the normal value combined with the land use type.

[0160] For example, the analysis steps and index calculation formula of the distribution density of post-disaster key service facilities (DCF) are as follows:

[0161] Step one: Based on Python crawler code and map open platform, obtain the POI of key service facilities including shopping facilities (supermarkets, shopping malls, convenience stores, etc.) and medical facilities (hospitals, clinics, community health centers, etc.) in the study area, remove the submerged POI to form the post-disaster un-submerged key service facility point file;

[0162] Step two: Use the kernel density analysis (KernelDensity) in ARCGIS to calculate the distribution density of key service facilities per square kilometer by formula (12-14).

[0163] (12)

[0164] (13)

[0165] (14)

[0166] wherein: is the kernel density value of the position ; is the number of POI points of the non-flooded critical service facilities; is the bandwidth parameter, which controls the range of the field when calculating the density; is the kernel function, which is used to weight each point, and the selected kernel function is the Gaussian kernel function ; is the coordinate of the th point in the point data set; is the Euclidean distance between the position and the th point; is the normalized distance, which is the value obtained by dividing the actual distance by the bandwidth ; takes the median of .

[0167] Exemplarily, the analysis steps and index calculation formula of the post-disaster critical service facility walk-up accessibility (ACF) are as follows:

[0168] Step one: read the vector data of the post-disaster critical service facilities and the street network for walk-up accessibility analysis generated in the “Analysis Steps and Index Calculation Formula of Street Network Walk-up Accessibility (ANS) in Disaster” using the geopandas library of Python;

[0169] Step two: calculate the distance from each critical service facility to all streets and find the nearest street using the distance method, and extract the value of the walk-up accessibility field from the nearest street, see formula (15).

[0170] (15)

[0171] wherein: is the walk-up accessibility of the street nearest to a certain non-flooded critical service facility.

[0172] Exemplarily, the analysis steps and index calculation formula of the submerged land disaster loss rate (DRU) are as follows:

[0173] Step one: record the area of each type of land use submerged and its land use vulnerability level (determined according to “HY / T0273-2019 Technical Guidelines for Marine Disaster Risk Assessment and Zoning Part 1: Storm Surge”);

[0174] Step two: calculate the submerged land disaster loss rate by formula (16).

[0175] (16)

[0176] wherein: is the index of land use type, divided into 1 to types; the weight of each land use type is assigned according to different vulnerability levels of land use as shown in Table 5 below; is the flooded area of the th land use type; is the total flooded area of the th land use type.

[0177] Table 5: Calculation weight of disaster damage rate of each land use type

[0178]

[0179] The embodiment of the present application constructs a unified index system containing various morphological elements of the city, integrates factors such as landscape ecological space, street network topology, and block building function layout, fully considers the role of key spatial parameters such as landscape ecological space layout, street network organization, and land and facility layout in the urban flood resilience, so as to more comprehensively and accurately evaluate the ability of the city to cope with flood disasters, provide accurate reference for the accuracy and pertinence of urban resilience governance intervention measures, and improve the flood resilience of the city.

[0180] In another preferred embodiment, the S5, according to the first resilience index, the second resilience index and the third resilience index, calculates the flood resilience comprehensive evaluation result of each flood scenario of the to-be-evaluated region, including:

[0181] S51, the first resilience index, the second resilience index and the third resilience index of each flood scenario are standardized;

[0182] S52, according to the weighted sum of the first resilience index, the second resilience index and the third resilience index after standardization, the flood resilience comprehensive evaluation result of each flood scenario of the to-be-evaluated region is obtained.

[0183] Specifically, the embodiment of the present application integrates the above resilience indexes to form a comprehensive flood resilience evaluation system of urban spatial morphological elements, as shown in Table 6 below.

[0184] Table 6: Comprehensive flood resilience evaluation system of urban spatial morphological elements

[0185]

[0186] The first, second, and third resilience indices for each flood scenario were standardized. A weighted sum of the standardized first, second, and third resilience indices was then performed to obtain the comprehensive flood resilience assessment result for each flood scenario in the area to be assessed. This comprehensive flood resilience assessment result includes the comprehensive resilience assessment results for the landscape ecology, street network, building blocks, and overall resilience.

[0187] For example, the first, second, and third resilience indices with different sources, dimensions, and orders of magnitude in flood scenarios 1-6 are standardized. Then, the entropy weight method is used to determine the weights of the indices to reflect the relative importance of each indices in flood resilience assessment. In this way, a quantitative model of the spatial morphology flood resilience of high-density coastal urban areas is established. The process is shown in formulas (17-21).

[0188] (17)

[0189] (18)

[0190] (19)

[0191] (20)

[0192] (twenty one)

[0193] in: It is the first The first flood scenario sample The value of each indicator; It is the weight of each indicator. It refers to the number of flood scenario samples; It is the information entropy of each indicator. It is the difference coefficient for each indicator; The weights of each indicator are determined by the entropy weight method. It refers to the number of indicators.

[0194] By multiplying the standardized indicators by their respective weights using formula (22) and then summing the results, the comprehensive flood resilience assessment result for each flood scenario is obtained. As shown in Table 7 below.

[0195] (twenty two)

[0196] in: It is the first The results of a comprehensive assessment of flood resilience under various flood scenarios.

[0197] Table 7 Comprehensive evaluation of urban spatial form element layer flood disaster multi-scenario resilience index.

[0198]

[0199] The embodiment of the application integrates big data, hydrodynamic models, GIS spatial analysis, Python data processing and other technologies, and realizes efficient processing and analysis of multi-source data. The embodiment of the application constructs a multi-scenario urban flood disaster simulation model, and couples a Mike 11 one-dimensional river module, a Mike 21 two-dimensional surface flow module and a MikeUrban pipe network module in Mike Flood. This three-dimensional dynamic coupling method can fully consider the interaction between different spatial elements, more accurately simulate the dynamic process of flood in the complex urban environment, and thus improve the accuracy and reliability of the simulation. The embodiment of the application constructs a multi-scenario evaluation method, comprehensively considers various natural disaster factors and their interactions, including rainfall, surface runoff, pipe network drainage, river flood routing and other multi-process complex actions, and can more comprehensively evaluate the flood risk of the city under different disaster scenarios. The embodiment of the application constructs a unified index system containing urban spatial form elements, comprehensively considers the effects of different urban spatial elements on flood resilience, deeply analyzes the effects of key spatial parameters such as green space permeability and street accessibility on urban flood resilience, and thus optimizes the urban form through spatial analysis methods. In this way, the flood resilience level of the city can be more accurately evaluated, thereby providing more targeted suggestions for urban adaptive planning and design.

[0200] Correspondingly, the application also provides a kind of urban flood resilience evaluation device, can realize the all processes of urban flood resilience evaluation method in the above-mentioned embodiment.

[0201] Please refer to Figure 3 , Figure 3 It is a preferred embodiment of the structure schematic view of the urban flood resilience evaluation device provided by the application. The urban flood resilience evaluation device comprises:

[0202] The data acquisition module 301 is configured to acquire hydro-meteorological data of a region to be evaluated and geographic information data of a city spatial form element layer, wherein the hydro-meteorological data includes water level data, rainfall data, typhoon data and SLR prediction data; and the city spatial form element layer includes a landscape ecological space element layer, a street network space element layer and a block building space element layer.

[0203] The model construction module 302 is configured to set boundary conditions of multiple flood scenarios according to the hydro-meteorological data, and construct a multi-scenario urban flood disaster simulation model in combination with geographic information data of the urban spatial form element layer; wherein the multi-scenario urban flood disaster simulation model comprises river-ground-pipe network coupling models of the multiple flood scenarios.

[0204] The flood simulation module 303 is configured to generate flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of each of the multiple flood scenarios respectively according to the multi-scenario urban flood disaster simulation model.

[0205] The index calculation module 304 is configured to calculate a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer and a third resilience index of the block building space element layer of each of the multiple flood scenarios according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of each of the multiple flood scenarios.

[0206] The comprehensive evaluation module 305 is configured to calculate a flood resilience comprehensive evaluation result of each of the multiple flood scenarios of the to-be-evaluated region according to the first resilience index, the second resilience index and the third resilience index.

[0207] Preferably, the geographic information data comprises terrain data, urban water body and green space system data, urban street network data, municipal drainage pipe network data, urban land use data, POI data of emergency shelters and POI data of key service facilities.

[0208] Preferably, the model construction module 302 is specifically configured to:

[0209] set boundary conditions of multiple flood scenarios according to the water level data, the rainfall data, the typhoon data and the SLR prediction data; wherein the boundary conditions of the multiple flood scenarios are sequentially increased;

[0210] construct river models of the multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of the multiple flood scenarios;

[0211] construct ground models of the multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of the multiple flood scenarios;

[0212] construct pipe network models of the multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of the multiple flood scenarios;

[0213] The riverway model, the surface model and the pipe network model of the plurality of flood scenarios are coupled to obtain the multi-scenario urban flood disaster simulation model.

[0214] Preferably, the index calculation module 304 is specifically configured to:

[0215] The flood submergence depth data of the landscape ecological space element layer of the plurality of flood scenarios is combined with the urban water body and green space system data to generate corresponding landscape ecological space submergence distribution data;

[0216] According to the landscape ecological space submergence distribution data, the first resilience index of the landscape ecological space element layer of each of the flood scenarios is calculated;

[0217] The flood submergence depth data of the street network space element layer of the plurality of flood scenarios is combined with the urban street network data and the POI data of the emergency shelter to generate corresponding street network space submergence distribution data;

[0218] According to the street network space submergence distribution data, the second resilience index of the street network space element layer of each of the flood scenarios is calculated;

[0219] The flood submergence depth data of the block building space element layer of the plurality of flood scenarios is combined with the urban street network data, the urban land use data and the POI data of the key service facility to generate corresponding block building space submergence distribution data;

[0220] According to the block building space submergence distribution data, the third resilience index of the block building space element layer of each of the flood scenarios is calculated.

[0221] Preferably, the first resilience index includes a landscape ecological space and submergence range overlap degree, a flood peak reduction rate and a flood peak delay time;

[0222] The second resilience index includes a disaster street network walkability, a disaster emergency shelter distribution density, a disaster emergency shelter walkability and a submergence street network disaster damage rate;

[0223] The third resilience index includes a post-disaster key service facility distribution density, a post-disaster key service facility walkability and a submergence land disaster damage rate.

[0224] Preferably, the comprehensive evaluation module 305 is specifically configured to:

[0225] The first resilience index, the second resilience index and the third resilience index of each of the flood scenarios are standardized;

[0226] The first, second and third resilience indexes are weighted and summed to obtain a comprehensive evaluation result of the flood resilience of each flood scenario of the to-be-evaluated region.

[0227] In a specific implementation, the working principle, control flow and technical effects of the urban flood resilience evaluation device provided by the embodiments of the present application are the same as those of the urban flood resilience evaluation method in the above embodiments, and thus will not be described here.

[0228] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of a preferred embodiment of a terminal device provided by the present application. The terminal device comprises a processor 401, a memory 402, and a computer program stored in the memory 402 and configured to be executed by the processor 401, wherein the processor 401 implements the urban flood resilience evaluation method described in any of the above embodiments when executing the computer program.

[0229] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, …), which are stored in the memory 402 and executed by the processor 401 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0230] The processor 401 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 401 can also be any conventional processor. The processor 401 is the control center of the terminal device, and connects various parts of the terminal device through various interfaces and lines.

[0231] The memory 402 mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like, and the data storage area can store related data and the like. In addition, the memory 402 can be a high-speed random access memory, and can also be a non-volatile memory such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like, or the memory 402 can also be other volatile solid-state storage devices.

[0232] It should be noted that the terminal device described above can include, but is not limited to, a processor and a memory, and those skilled in the art can understand that, Figure 4 The structural diagram is only an example of the terminal device described above, and does not constitute a limitation on the terminal device described above, and can include more or fewer components than the diagram, or combine certain components, or different components.

[0233] The embodiment of the present application also provides a computer readable storage medium, the computer readable storage medium includes a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the urban flood resilience assessment method described in any of the above embodiments.

[0234] The embodiment of the present application also provides a computer program product, the computer program product includes a computer program or computer instructions, when the computer program or the computer instructions are executed by a processor, the urban flood resilience assessment method described in any of the above embodiments is realized.

[0235] This invention provides a method, apparatus, equipment, medium, and computer program product for assessing urban flood resilience. It acquires hydrological and meteorological data of the area to be assessed, as well as geographic information data of the urban spatial morphology element layer. The hydrological and meteorological data includes water level data, rainfall data, typhoon data, and SLR prediction data. The urban spatial morphology element layer includes a landscape ecological spatial element layer, a street network spatial element layer, and a block building spatial element layer. Based on the hydrological and meteorological data, boundary conditions for multiple flood scenarios are set, and combined with the geographic information data of the urban spatial morphology element layer, a multi-scenario urban flood disaster simulation model is constructed. The multi-scenario urban flood disaster simulation model includes multiple river-surface-pipeline coupling models for various flood scenarios. Based on the multi-scenario urban flood disaster simulation model, flood inundation depth data for the landscape ecological space element layer, street network space element layer, and block building space element layer of multiple flood scenarios are generated respectively. Based on the flood inundation depth data of the landscape ecological space element layer, street network space element layer, and block building space element layer of multiple flood scenarios, a first resilience index, a second resilience index, and a third resilience index of the landscape ecological space element layer, street network space element layer, and block building space element layer of each flood scenario are calculated. Based on the first resilience index, the second resilience index, and the third resilience index, the comprehensive flood resilience assessment result for each flood scenario in the area to be assessed is calculated. This embodiment of the invention establishes an assessment system for multiple composite flood disaster scenarios, comprehensively considering different disaster factors and their interactions, and fully considering the role of key spatial parameters in urban flood resilience, thereby enabling a more comprehensive and accurate assessment of the city's ability to cope with flood disasters.

[0236] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0237] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for urban flood resilience assessment, characterized in that, The method comprises the following steps: obtaining hydro-meteorological data of an area to be evaluated and geographic information data of a city spatial form element layer; wherein the hydro-meteorological data comprises water level data, rainfall data, typhoon data and sea level rise (SLR) prediction data; and the city spatial form element layer comprises a landscape ecological space element layer, a street network space element layer and a block building space element layer; setting boundary conditions of multiple flood scenarios according to the hydro-meteorological data, and constructing a multi-scenario city flood disaster simulation model in combination with the geographic information data of the city spatial form element layer; wherein the multi-scenario city flood disaster simulation model comprises river-ground-pipe network coupling models of multiple flood scenarios; generating flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios respectively according to the multi-scenario city flood disaster simulation model; calculating a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer and a third resilience index of the block building space element layer of each of the flood scenarios according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer and the block building space element layer of multiple flood scenarios; calculating a comprehensive evaluation result of the flood resilience of each of the flood scenarios of the area to be evaluated according to the first resilience index, the second resilience index and the third resilience index; wherein the step of setting boundary conditions of multiple flood scenarios according to the hydro-meteorological data, and constructing a multi-scenario city flood disaster simulation model in combination with the geographic information data of the city spatial form element layer comprises: setting boundary conditions of multiple flood scenarios according to the water level data, the rainfall data, the typhoon data and the SLR prediction data; wherein the boundary conditions of the multiple flood scenarios are sequentially increased; constructing river models of multiple flood scenarios based on the boundary conditions of the multiple flood scenarios in combination with the geographic information data of the city spatial form element layer; constructing ground models of multiple flood scenarios based on the boundary conditions of the multiple flood scenarios in combination with the geographic information data of the city spatial form element layer; constructing pipe network models of multiple flood scenarios based on the boundary conditions of the multiple flood scenarios in combination with the geographic information data of the city spatial form element layer; coupling the river models, the ground models and the pipe network models of the multiple flood scenarios to obtain the multi-scenario city flood disaster simulation model.

2. The urban flood resilience assessment method of claim 1, wherein, The geographic information data comprises terrain data, city water body and green space system data, city street network data, municipal drainage pipe network data, city land use data, POI data of emergency shelters and POI data of key service facilities.

3. The urban flood resilience assessment method of claim 2, wherein, The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer according to a plurality of flood scenarios are combined with the urban water body and green space system data to generate corresponding landscape ecological space inundation distribution data. The first resilience index includes the overlap degree of the landscape ecological space and the inundation range, the flood peak reduction rate, and the flood peak delay time.

4. The urban flood resilience assessment method of claim 3, wherein, The second resilience index includes the in-disaster street network walkability, the in-disaster emergency shelter distribution density, the in-disaster emergency shelter walkability, and the inundated street network disaster damage rate. The third resilience index includes the post-disaster key service facility distribution density, the post-disaster key service facility walkability, and the inundated land disaster damage rate. The first resilience index includes the overlap degree of the landscape ecological space and the inundation range, the flood peak reduction rate, and the flood peak delay time.

5. The urban flood resilience assessment method of claim 4, wherein, The first resilience index includes the overlap degree of the landscape ecological space and the inundation range, the flood peak reduction rate, and the flood peak delay time. The data acquisition module is configured to acquire hydro-meteorological data and geographic information data of urban spatial form element layers of an area to be evaluated, wherein the hydro-meteorological data includes water level data, rainfall data, typhoon data, and SLR prediction data, and the urban spatial form element layers include a landscape ecological space element layer, a street network space element layer, and a block building space element layer. ​ 6. An urban flood resilience assessment device, characterized by, ​ ​ A model construction module is configured to set boundary conditions of multiple flood scenarios according to the hydro-meteorological data, and construct a multi-scenario urban flood disaster simulation model in combination with geographic information data of the urban spatial form element layer; wherein the multi-scenario urban flood disaster simulation model comprises river-surface-pipe network coupling models of multiple flood scenarios; A flood simulation module is configured to generate flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of multiple flood scenarios respectively according to the multi-scenario urban flood disaster simulation model; An index calculation module is configured to calculate a first resilience index of the landscape ecological space element layer, a second resilience index of the street network space element layer, and a third resilience index of the block building space element layer of each of the flood scenarios according to the flood inundation depth data of the landscape ecological space element layer, the street network space element layer, and the block building space element layer of multiple flood scenarios; A comprehensive evaluation module is configured to calculate a flood resilience comprehensive evaluation result of each of the flood scenarios of the to-be-evaluated region according to the first resilience index, the second resilience index, and the third resilience index; The model construction module is specifically configured to: set boundary conditions of multiple flood scenarios according to the water level data, the rainfall data, the typhoon data, and the SLR prediction data; wherein the boundary conditions of multiple flood scenarios are sequentially increased; construct river models of multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of multiple flood scenarios; construct surface models of multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of multiple flood scenarios; construct pipe network models of multiple flood scenarios in combination with the geographic information data of the urban spatial form element layer based on the boundary conditions of multiple flood scenarios; couple the river models, the surface models, and the pipe network models of multiple flood scenarios to obtain the multi-scenario urban flood disaster simulation model.

7. A terminal device, characterized by comprising: A processor and a memory are included, the memory stores a computer program, and the computer program is configured to be executed by the processor, and the processor implements the urban flood resilience evaluation method in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the device where the computer readable storage medium is located implements the urban flood resilience evaluation method in any one of claims 1 to 5 when executing the computer program.

9. A computer program product, characterised in that, The computer program product includes a computer program or computer instructions, and the computer program or the computer instructions are executed by the processor to implement the urban flood resilience evaluation method in any one of claims 1 to 5.

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