An adaptive strategy optimization method based on urban flood resilience

Abstract

The present application provides an adaptive strategy optimization method based on urban flood resilience, which is applied to the field of disaster emergency response technology. The method includes: determining urban flood risk areas by analyzing geographic spatial data; determining the adaptive measure distribution of adaptive measures in flood risk areas by pre-set constraints of adaptive measures; determining the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution according to the mapping relationship between the adaptive strategy type to be evaluated and the target, and obtaining the target adaptive measure distribution; inputting the measure parameter information and rainfall data information of the target adaptive measure distribution under the target rainfall scenario into the urban flood resilience simulation model for simulation to determine the flood resilience simulation results; determining the target adaptive strategy type based on the flood resilience simulation results, and determining the corresponding target adaptive measure distribution as the optimal adaptive strategy. The purpose is to evaluate the best performing adaptive strategy under urban rainfall scenarios.

Description

Technical Field

[0001] The present application relates to the field of disaster emergency response technology, and in particular to an adaptive strategy optimization method based on urban flood resilience. Background Art

[0002] In research on the selection and optimization of flood adaptation strategies, the key to developing adaptive strategies lies in how to choose the most effective ones, how to evaluate the actual effects of implemented adaptive strategies, and how to optimize and adjust adaptive strategies based on the evaluation results. Current research on the selection and optimization of flood adaptation strategies largely remains at the qualitative analysis level, lacking dynamic feedback from rainfall inundation processes as a basis for strategy selection. This makes it impossible to fully identify and understand the complex dynamic behavior of urban systems in the face of flood disasters. Furthermore, existing adaptive strategy implementation frameworks fail to provide a clear methodology or evaluation tools to guide the selection and adjustment of strategies in different regions in practice. This limits the long-term effectiveness of adaptive strategies or plans in responding to more complex or severe flood scenarios that may arise in the future. Summary of the Invention

[0003] In light of this, this application proposes an adaptive strategy optimization method based on urban flood resilience. It aims to evaluate and determine the best adaptive strategy among various strategies under specific urban rainfall scenarios, providing a basis for decision-making tailored to local conditions in urban planning.

[0004] In a first aspect of an embodiment of the present application, a method for optimizing an adaptive strategy based on urban flood resilience is provided, the method comprising:

[0005] Identify flood risk areas in the city by analyzing the city’s geospatial data;

[0006] By using the preset constraints of various adaptive measures, the distribution of various adaptive measures in the flood risk area is determined, and the distribution of adaptive measures in the flood risk area is obtained;

[0007] According to the adaptive strategy type to be evaluated and the target mapping relationship, determining the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution, and obtaining the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area, wherein the target mapping relationship records the correspondence between various adaptive measures and various adaptive strategy types;

[0008] Inputting the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario;

[0009] Based on the urban flood resilience simulation results of various adaptation strategy types under the target rainfall scenario, determine the target adaptation strategy type under the target rainfall scenario;

[0010] The target adaptation measure distribution corresponding to the target adaptation strategy type under the target rainfall scenario is determined as the optimal adaptation strategy under the target rainfall scenario.

[0011] Optionally, the distribution of various adaptive measures in the flood risk area is determined by considering the preset constraints of each adaptive measure, and the distribution of adaptive measures in the flood risk area is obtained, including:

[0012] Determine the initial distribution of various adaptation measures in flood risk areas based on their respective geo-hydrological constraints;

[0013] The initial distribution is screened according to the user-defined constraints of various adaptive measures to determine the distribution of adaptive measures in the flood risk area.

[0014] Optionally, according to the mapping relationship between the adaptive strategy type to be evaluated and the target, determining the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution, and obtaining the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area, includes:

[0015] According to the mapping relationship between the adaptive strategy type to be evaluated and the target, the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated is screened from the adaptive measure distribution;

[0016] The distribution of the various adaptive measures screened out is determined as the target adaptive measure distribution in the flood risk area corresponding to the adaptive strategy type to be evaluated, and the adaptive strategy type includes at least: green stormwater street strategy type, green space expansion strategy type and green infrastructure integration strategy type.

[0017] Optionally, build an urban flood resilience simulation model, including:

[0018] Constructing a data preprocessing unit for the urban flood resilience simulation model to preprocess the geographic data required to determine urban flood resilience;

[0019] Construct a rainfall process determination unit for the urban flood resilience simulation model, which is used to determine the rainfall process information under the target rainfall scenario based on the set rainfall intensity formula;

[0020] Constructing a flood inundation determination unit of the urban flood resilience simulation model, which is used to simulate and analyze the rainfall process information and pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information;

[0021] Constructing a flood resilience determination unit in the urban flood resilience simulation model to determine the city's flood resilience based on the city's time-series inundation depth data;

[0022] Based on the constructed data preprocessing unit, rainfall process determination unit, flood inundation determination unit and flood resilience determination unit, an urban flood resilience simulation model is obtained.

[0023] Optionally, the measure parameter information of the target adaptive measure distribution adopted under the target rainfall scenario and the rainfall data information of the target rainfall scenario are input into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of adopting the target adaptive measure distribution under the target rainfall scenario, including:

[0024] Matching and setting various parameters of the surface layer, pavement layer, soil layer, and aquifer of the adaptive measures with different distribution shapes in the target adaptive measure distribution to obtain measure parameter information for adopting the target adaptive measure distribution. The measure parameter information for adopting the target adaptive measure distribution is part of the geographic data required to determine urban flood resilience.

[0025] Inputting the target adaptive measure distribution parameter information and the target rainfall scenario rainfall data information into the urban flood resilience simulation model for simulation calculation;

[0026] Preprocessing the geographic data required for determining urban flood resilience by the data preprocessing unit of the urban flood resilience simulation model;

[0027] The rainfall process determination unit of the urban flood resilience simulation model determines the rainfall process information under the target rainfall scenario based on a set rainfall intensity formula;

[0028] The flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the rainfall process information and the pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information;

[0029] The flood resilience determination unit of the urban flood resilience simulation model is used to determine the flood resilience of the city based on the city's time-series inundation depth data.

[0030] Optionally, a flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the rainfall process information and the preprocessed geographic data based on a storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information, including:

[0031] The water drop points of the urban pipe network data in the pre-processed geographic data are divided into corresponding sub-catchment areas through a preset algorithm;

[0032] The average slope and impervious area ratio of the subcatchment were calculated by ArcGIS spatial analysis;

[0033] Conduct coupled simulation of one-dimensional and two-dimensional hydrodynamic models through the storm flood management model, and set simulation parameters;

[0034] Based on the rainfall process information, preprocessed geographic data, the average slope and impervious area ratio of the sub-catchment, and taking the sub-catchment as the simulation unit, simulation calculations are performed through the time synchronization and water exchange mechanism of the coupled one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to determine the time-series flooding depth data of the city under the rainfall process information.

[0035] Optionally, the flood resilience determination unit of the urban flood resilience simulation model can be used to determine the flood resilience of the city based on the city's time-series inundation depth data, including:

[0036] The flood resilience determination unit of the urban flood resilience simulation model determines the corresponding time-series traffic reliability data based on the city's time-series flood depth data;

[0037] The urban system performance curve is determined by substituting all independent paths and time sequence traffic reliability data in the city into the urban system performance quantification algorithm for calculation. The urban system performance quantification algorithm expression is: ,in The quantitative index value representing the performance of the urban system at time t; represents an independent path node pair in the urban road network, that is, the shortest path between the i-th node and the j-th node in the road network is an independent path; n is the total number of nodes included in all independent paths; is the node weight; is the path weight; Indicates independent paths The value of traffic reliability at time t; is the number of all independent paths between the i-th node and the j-th node; where the traffic reliability at time t is To form independent paths The reliability of traffic on each road section The product of

[0038] The flood resilience of a city is determined by the ratio of the integral of the urban system performance curve on the time axis from the start of the flood disaster to the recovery of the urban system to a stable state after the disaster to the integral of the urban system performance curve on the time axis when no flood disaster occurs.

[0039] Optionally, determine independent paths in the urban road network, including:

[0040] Determine the bifurcation position of a bifurcated road in an urban road as a road node, and construct the two road nodes into a node pair;

[0041] The target algorithm is used to cyclically search for a new shortest path among the remaining paths between node pairs that does not overlap with the shortest path previously searched;

[0042] All the shortest paths obtained by searching are determined as independent paths, and independent paths in the urban road network are obtained.

[0043] Optionally, based on the urban flood resilience simulation results of various adaptation strategy types under the target rainfall scenario, the target adaptation strategy type under the target rainfall scenario is determined, including:

[0044] By substituting the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario into the cost-benefit index algorithm for calculation, the unit cost flood resilience improvement value of each adaptive strategy type under the target rainfall scenario is determined. The cost-benefit index algorithm expression is: , where I is the degree of improvement in flood resilience per unit cost; The flood resilience of the city after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the flood resilience of cities that do not implement adaptive strategies under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategies under the target rainfall scenario;

[0045] The adaptive strategy type with the largest improvement value in flood resilience under unit cost is determined as the target adaptive strategy type under the target rainfall scenario.

[0046] Compared with the prior art, this application has the following advantages:

[0047] An embodiment of the present application provides an adaptive strategy optimization method based on urban flood resilience. First, the city's geospatial data is analyzed to determine the city's flood risk areas; the distribution of various adaptive measures in the flood risk areas is determined by the preset constraints of various adaptive measures, and the distribution of adaptive measures in the flood risk areas is obtained; according to the adaptive strategy type to be evaluated and the target mapping relationship, the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution is determined, and the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area is obtained. The target mapping relationship records the correspondence between various adaptive measures and various adaptive strategy types; the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario are input into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario; based on the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario, the target adaptive strategy type under the target rainfall scenario is determined; the target adaptive measure distribution corresponding to the target adaptive strategy type under the target rainfall scenario is determined as the optimal adaptive strategy under the target rainfall scenario. Therefore, this application first determines the areas in the city that are at risk of flood disasters, and then evaluates the areas to improve the evaluation efficiency. Then, for the determined flood risk areas, the distribution of various adaptive measures is determined. Then, based on the target mapping relationship between various adaptive measures and various adaptive strategy types, the target adaptive measure distribution corresponding to the adaptive strategy type is determined. Then, based on the target adaptive measure distribution, the flood resilience of the city under the influence of the target adaptive measure distribution is determined. Based on the different flood resilience performances corresponding to different adaptive strategy types, the target adaptive measure distribution corresponding to one of the best performing adaptive strategy types is selected as the optimal adaptive strategy. In this way, the adaptive strategy with the best performance under the corresponding urban rainfall scenario can be evaluated and determined, providing a decision-making basis for urban planning that is adapted to local conditions.

[0048] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0050] Figure 1A flowchart of an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the distribution of various adaptive measures in flood risk areas in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0052] Figure 3 Another schematic diagram of the distribution of various adaptive measures in flood risk areas in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the performance of an urban system without taking adaptive measures in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of urban system performance under the distribution of target adaptive measures corresponding to the green stormwater street strategy type in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of urban system performance under the distribution of target adaptive measures corresponding to green space expansion strategy types in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application;

[0056] Figure 7 A schematic diagram of urban system performance under the distribution of target adaptive measures corresponding to the green infrastructure integration strategy type in an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings.

[0058] Figure 1 A flowchart of an adaptive strategy optimization method based on urban flood resilience provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0059] Step S1: Determine the flood risk areas of the city by analyzing the city's geographic spatial data.

[0060] In this embodiment, in order to improve the efficiency of evaluating the adaptive strategy of a city, the present application determines the distribution of adaptive measures for the flood risk areas of the city and conducts subsequent evaluations. Specifically, the geographic spatial data of the city is obtained, and the geographic spatial data at least includes land use data, water system distribution data, road status data, traffic flow data, and public facilities distribution data. By analyzing the geographic spatial data of the city, the potential flood risk areas in the city are determined. These potential risk areas are areas in the city with a greater risk of flooding or areas where flooding will have a serious impact. For example, the spatial distribution of various land use types and their relationship with flood risk are analyzed to identify potential flood risk points, and a certain range including the potential flood risk points is determined as a flood risk area; the traffic capacity of the road network and its weak links in flood disasters are evaluated, and a certain range including the weak links is determined as a flood risk area; the spatial distribution of human activities and key protection areas are identified and determined as flood risk areas. Among them, there may be multiple flood risk areas in a single city.

[0061] Step S2: Determine the distribution of various adaptive measures in the flood risk area through the preset constraints of each adaptive measure, and obtain the distribution of adaptive measures in the flood risk area.

[0062] In this embodiment, a preset constraint condition corresponding to each adaptive measure is pre-set. Then, after the flood risk area in the city is determined in step S1, the preset constraint conditions of each adaptive measure are used to determine the locations in the flood risk area where the various adaptive measures can be implemented, thereby obtaining the distribution of the various adaptive measures in the flood risk area of the city. This distribution is the distribution of the adaptive measures in the flood risk area of the city, such as Figure 2 As shown, Figure 2 This example shows the distribution of various adaptive measures within a city's flood risk zones and their locations within those zones. This distribution represents the distribution of adaptive measures within the city's flood risk zones. It should be understood that, if evaluation efficiency is neglected and an entire city is evaluated, the distribution of adaptive measures can also be determined within the city itself, and subsequent evaluations can be adjusted to evaluate adaptive strategies across the entire city.

[0063] Step S3: According to the adaptive strategy type to be evaluated and the target mapping relationship, determine the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution, and obtain the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area. The target mapping relationship records the correspondence between various adaptive measures and various adaptive strategy types.

[0064] In the present application, step S3 specifically includes: based on the mapping relationship between the adaptive strategy type to be evaluated and the target, screening out the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated from the adaptive measures distribution; determining the distribution of the screened out various adaptive measures as the target adaptive measures distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area, and the adaptive strategy types include at least: green stormwater street strategy type, green space expansion strategy type and green infrastructure integration strategy type.

[0065] In this embodiment, the present application predefines a variety of adaptive strategies, and the types of adaptive measures that can be taken will also be different for different adaptive strategy types. That is, which types of adaptive measures in a type of adaptive strategy can be adopted under the adaptive strategy will be predefined. Among them, the multiple adaptive strategies predefined in the present application include a green stormwater street strategy type, a green space expansion strategy type, and a green infrastructure integration strategy type. The adaptive measures that can be taken under the green stormwater street strategy type are all adaptive measures related to the street network, such as permeable pavement, street greening, and rain gardens. The adaptive measures that can be taken under the green space expansion strategy type are the types of adaptive measures that can be taken in built roads and block spaces where it is not easy to increase green space and water bodies on a large scale, such as green roofs, rain gardens, etc. The adaptive measures that can be taken under the green infrastructure integration strategy type are all types of adaptive measures.

[0066] It should be understood that the multiple adaptive strategies may also be other adaptive strategies, but each adaptive strategy type also has corresponding adaptive measures that can be taken under the adaptive strategy type.

[0067] In this embodiment, the present application pre-establishes a correspondence between various adaptive strategy types and various adaptive measures, which is a target mapping relationship. For the current adaptive strategy type to be evaluated, the various adaptive measures corresponding to the adaptive strategy type to be evaluated are determined through the mapping relationship between the various adaptive strategy types and various adaptive measures recorded in the target mapping relationship. Then, for the total adaptive measure distribution obtained through step S2, only the various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution are retained, thereby obtaining the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area. For example, Figure 2 As shown, in the case where the various adaptive measures corresponding to the adaptive strategy type A to be evaluated recorded in the target mapping relationship include a1 type adaptive measures and a2 type adaptive measures, based on the determination as follows Figure 2The total adaptive measure distribution in the flood risk area of the city shown in FIG is retained only by retaining the a1 type adaptive measures and a2 type adaptive measures corresponding to the adaptive strategy type A to be evaluated in the total adaptive measure distribution. The adaptive measure distribution obtained is the adaptive measure distribution corresponding to the adaptive strategy type A to be evaluated in the flood risk area of the city, as shown in FIG. Figure 3 The figure shows the obtained adaptive measure distribution corresponding to the adaptive strategy type A to be evaluated. For each adaptive strategy type to be evaluated, the corresponding target adaptive measure distribution can be determined through the same implementation method of step S3.

[0068] Step S4: Input the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario.

[0069] In this embodiment, the target rainfall scenario is a rainstorm and flood event scenario with a specific recurrence period. The specific period can be any period, such as a rainstorm and flood event scenario with a 5-year recurrence period, a rainstorm and flood event scenario with a 20-year recurrence period, a rainstorm and flood event scenario with a 50-year recurrence period, and a rainstorm and flood event scenario with a 100-year recurrence period. The recurrence period refers to the average number of years it takes for a flood disaster event of this magnitude to occur.

[0070] In this embodiment, the present application pre-constructs an urban flood resilience simulation model, and inputs the rainfall data information under the target rainfall scenario of the city (the rainfall data information is the rainfall intensity per minute, in units of mm / min), and the measure parameter information corresponding to the target adaptive measure distribution taken in the flood risk area of the city (the target adaptive measure distribution is determined by step S3) into the urban flood resilience simulation model for simulation calculation to obtain the flood resilience value of the city. The flood resilience value represents the urban resilience performance of the city after the target adaptive measure distribution is taken in the city under the influence of the target rainfall scenario. The larger the value, the better the urban resilience performance, indicating that the city's performance in responding to flood disasters has been effectively improved. Specifically, the rainfall data information under the target rainfall scenario and the measure parameter information corresponding to the target adaptive measure distribution under the adaptive strategy type to be evaluated are input into the pre-constructed urban flood resilience simulation model for simulation calculation to obtain the flood resilience value of the city, which is the corresponding urban flood resilience simulation result. Therefore, under the target rainfall scenario, for the target adaptive measure distribution under each adaptive strategy type to be evaluated, the corresponding flood resilience value can be obtained by simulation calculation through the same implementation method of step S4.

[0071] Step S5: Based on the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario, determine the target adaptive strategy type under the target rainfall scenario.

[0072] In this embodiment, after obtaining the simulation results of urban flood resilience for each adaptive strategy type under the target rainfall scenario through calculation in step S4, an optional implementation method is to determine the adaptive strategy type with the largest urban flood resilience value obtained by simulation as the target adaptive strategy type under the target rainfall scenario.

[0073] Step S6: determining the target adaptive measure distribution corresponding to the target adaptive strategy type under the target rainfall scenario as the optimal adaptive strategy under the target rainfall scenario.

[0074] In this embodiment, after determining the target adaptability strategy type under the target rainfall scenario through step S5, the target adaptability measure distribution corresponding to the target adaptability strategy type is determined as the optimal adaptability strategy under the target rainfall scenario. The optimal adaptability strategy is actually the locations in the flood risk areas of the city where various adaptability measures are set, as recorded in the target adaptability measure distribution.

[0075] An embodiment of the present application provides an adaptive strategy optimization method based on urban flood resilience. First, the city's geospatial data is analyzed to determine the city's flood risk areas; the distribution of various adaptive measures in the flood risk areas is determined by the preset constraints of various adaptive measures, and the distribution of adaptive measures in the flood risk areas is obtained; according to the adaptive strategy type to be evaluated and the target mapping relationship, the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution is determined, and the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area is obtained. The target mapping relationship records the correspondence between various adaptive measures and various adaptive strategy types; the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario are input into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario; based on the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario, the target adaptive strategy type under the target rainfall scenario is determined; the target adaptive measure distribution corresponding to the target adaptive strategy type under the target rainfall scenario is determined as the optimal adaptive strategy under the target rainfall scenario. Therefore, this application first determines the areas in the city that are at risk of flood disasters, and then evaluates the areas to improve the evaluation efficiency. Then, for the determined flood risk areas, the distribution of various adaptive measures is determined. Then, based on the target mapping relationship between various adaptive measures and various adaptive strategy types, the target adaptive measure distribution corresponding to the adaptive strategy type is determined. Then, based on the target adaptive measure distribution, the flood resilience of the city under the influence of the target adaptive measure distribution is determined. Based on the different flood resilience performances corresponding to different adaptive strategy types, the target adaptive measure distribution corresponding to one of the best performing adaptive strategy types is selected as the optimal adaptive strategy. In this way, the adaptive strategy with the best performance under the corresponding urban rainfall scenario can be evaluated and determined, providing a decision-making basis for urban planning that is adapted to local conditions.

[0076] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, step S2 may include steps S21 to S22:

[0077] Step S21: Determine the initial distribution of various adaptation measures in the flood risk area based on their respective geographical and hydrological constraints.

[0078] In this example, the basic data for a city's flood risk zones is loaded into the BMP site selection tool in the required format. By setting the geo-hydrological constraints for each adaptive measure in the model, the various adaptive measures are spatially suited within the city's flood risk zones, thereby obtaining an initial spatial distribution of these adaptive measures. The basic data includes at least drainage area data, slope data, impervious ratio data, road distance data, water system distance data, building distance data, and soil type data.

[0079] Step S22: screening the initial distribution according to the user-defined constraints of various adaptive measures to determine the distribution of adaptive measures for the flood risk area.

[0080] In this embodiment, after obtaining the initial distribution of various adaptive measures in the flood risk area of the city through step S21, specific values of each adaptive measure on multiple custom constraint types are set for each adaptive measure, and then the spatial analysis function of ArcGIS is used to screen out the adaptive measures that meet their corresponding custom constraints in the initial distribution based on the custom constraints of each adaptive measure. Then, all the adaptive measures screened out from the initial distribution constitute the final adaptive measure distribution of the flood risk area of the city. Among them, the multiple custom constraint types include but are not limited to single unit area, single unit width and land use nature. As shown in Table 1, Table 1 shows the specific geographical and hydrological constraints and specific custom constraints corresponding to some adaptive measures. It should be understood that some adaptive measures do not involve all or part of the geographical and hydrological constraints.

[0081] Table 1

[0082]

[0083] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, the method further includes step S01: constructing an urban flood resilience simulation model; step S01 may include steps S011 to S015:

[0084] Step S011: Constructing a data preprocessing unit of the urban flood resilience simulation model, which is used to preprocess the geographic data required to determine the urban flood resilience.

[0085] In this embodiment, a data preprocessing unit is constructed for the urban flood resilience simulation model. The data preprocessing unit is used to preprocess the various geographic data required to determine urban flood resilience, thereby obtaining preprocessed geographic data. The preprocessing includes at least converting and clipping the various geographic data to unify the coordinate system of the various geographic data and obtaining the geographic data of the flood risk area of the city to be assessed in a unified coordinate system; elevating the DEM data of buildings; generalizing the drainage network; and checking and confirming the topological relationship of the drainage network. The various geographic data include at least rainfall data, DEM data, drainage network data, land use type, road network data, underlying surface data, and river data.

[0086] Step S012: Construct a rainfall process determination unit of the urban flood resilience simulation model, which is used to determine the rainfall process information under the target rainfall scenario based on the set rainfall intensity formula.

[0087] In this embodiment, a rainfall process determination unit of the urban flood resilience simulation model is constructed. The rainfall process determination unit is used to calculate and determine the rainfall process information under the target rainfall scenario based on a set rainfall intensity formula. The rainfall process information under the target rainfall scenario refers to the amount of rainfall in the city under the target rainfall scenario and its distribution in time and space.

[0088] In this embodiment, the rainfall intensity formula is set as , where i is the rainfall intensity in mm / min; t is the rainfall duration in min; and T is the recurrence period in years.

[0089] Step S013: Construct a flood inundation determination unit of the urban flood resilience simulation model, which is used to simulate and analyze the rainfall process information and pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information.

[0090] In this embodiment, a flood inundation determination unit of an urban flood resilience simulation model is constructed. The flood inundation determination unit is used to simulate and analyze the rainfall process information under the target rainfall scenario and the geographic data preprocessed by the data preprocessing unit through a storm water management model, and determine the time-series inundation depth data of the city under the corresponding rainfall process information. The time-series inundation depth data refers to the inundation depth of the city at each moment in a continuous time period under the rainfall process information.

[0091] Step S014: Constructing a flood resilience determination unit of the urban flood resilience simulation model, which is used to determine the flood resilience of the city based on the city's time-series flood depth data.

[0092] In this embodiment, a flood resilience determination unit of an urban flood resilience simulation model is constructed, and the flood resilience determination unit is used to determine the flood resilience of a city based on the time-series flooding depth data of the city under the rainfall process information corresponding to the determined target rainfall scenario.

[0093] Step S015: Based on the constructed data preprocessing unit, rainfall process determination unit, flood inundation determination unit and flood resilience determination unit, an urban flood resilience simulation model is obtained.

[0094] In this embodiment, the data preprocessing unit, rainfall process determination unit, flood inundation determination unit and flood resilience determination unit constructed in steps S011 to S014 constitute an urban flood resilience simulation model for determining urban flood resilience.

[0095] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, step S4 may include steps S41 to S46:

[0096] Step S41: Match and set the various parameters of the surface layer, pavement layer, soil layer and aquifer of the adaptive measures with different shapes in the target adaptive measure distribution to obtain the measure parameter information of the target adaptive measure distribution. The measure parameter information of the target adaptive measure distribution is part of the geographic data required to determine the urban flood resilience.

[0097] In this embodiment, various parameters of the surface layer, pavement layer, soil layer and aquifer of various point-shaped, line-shaped and surface-shaped adaptive measures in the determined target adaptive measure distribution are matched and set to obtain the measure parameter information of the target adaptive measure distribution, wherein the measure parameter information of the target adaptive measure distribution is also part of the geographic data required to determine the urban flood resilience.

[0098] Step S42: Input the measure parameter information of the target adaptive measure distribution and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation.

[0099] In this embodiment, the measure parameter information of the target adaptive measure distribution and the rainfall data information under the target rainfall scenario, as well as various geographical data required to determine the urban flood resilience are input into the urban flood resilience simulation model for simulation calculation.

[0100] Step S43: Preprocessing the geographic data required for determining urban flood resilience through the data preprocessing unit of the urban flood resilience simulation model.

[0101] In this embodiment, various geographic data input into the urban flood resilience simulation model (the various geographic data also include parameter information of measures for the distribution of target adaptive measures) are preprocessed by the data preprocessing unit of the urban flood resilience simulation model to obtain preprocessed geographic data.

[0102] Step S44: The rainfall process determination unit of the urban flood resilience simulation model determines the rainfall process information under the target rainfall scenario based on the set rainfall intensity formula.

[0103] In this embodiment, the rainfall process information under the target rainfall scenario is calculated and determined based on a set rainfall intensity formula through the rainfall process determination unit of the urban flood resilience simulation model.

[0104] Step S45: The flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the rainfall process information and the pre-processed geographic data based on the storm flood management model to determine the time series inundation depth data of the city under the rainfall process information.

[0105] In this embodiment, the flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the pre-processed geographic data obtained in step S43 and the rainfall process information obtained in step S44 through the rainstorm flood management model to obtain the time-series inundation depth data of the city under the corresponding rainfall process information.

[0106] Step S46: Determine the flood resilience of the city based on the city’s time-series flood depth data through the flood resilience determination unit of the urban flood resilience simulation model.

[0107] In this embodiment, the flood resilience determination unit of the urban flood resilience simulation model analyzes and calculates the city's time-series flood depth data to determine the city's flood resilience value.

[0108] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, step S45 may include steps S451 to S454:

[0109] Step S451: Divide the pipe network water drop points of the urban pipe network data in the pre-processed geographic data into corresponding sub-catchment areas through a preset algorithm.

[0110] In this embodiment, each pipe network water outlet point in the urban pipe network data in the preprocessed geographic data is used as a node, and the sub-catchment area is divided according to the distribution of the nodes using the preset algorithm, the Thiessen polygon algorithm. Then, the obtained sub-catchment area is manually adjusted to obtain the final sub-catchment area division result.

[0111] Step S452: Calculate the average slope and impervious area ratio of the sub-catchment area through ArcGIS spatial analysis.

[0112] In this embodiment, based on the sub-catchment division result obtained in step S451, the average slope and impervious area ratio of each sub-catchment are calculated through ArcGIS spatial analysis.

[0113] Step S453: performing coupled simulation of the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model through the storm flood management model, and setting simulation parameters.

[0114] In this example, a coupled simulation of a one-dimensional and two-dimensional hydrodynamic model is performed using the SWMM Storm Water Management Model. Simulation parameters are set during the simulation process, including at least the total duration, time step, and Manning's roughness coefficient. The one-dimensional hydrodynamic model simulates the flow characteristics of water within a one-dimensional space, such as pipes and channels, in an urban drainage system. The two-dimensional hydrodynamic model simulates and predicts hydrodynamic characteristics such as water flow, water level changes, and velocity distribution.

[0115] Step S454: Based on the rainfall process information, preprocessed geographic data, the average slope and impervious area ratio of the sub-catchment, and taking the sub-catchment as the simulation unit, simulation calculations are performed through the time synchronization and water exchange mechanism of the coupled one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to determine the time-series flooding depth data of the city under the rainfall process information.

[0116] In this embodiment, based on the rainfall process information obtained in step S44, the preprocessed geographic data obtained in step S43, and the average slope and impervious area ratio of each sub-catchment obtained in step S452, the sub-catchment is used as a simulation unit, and the flooding depth is simulated and calculated through the time synchronization and water exchange mechanism of the coupled one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to determine the time-series flooding depth data of the city under the corresponding rainfall process information.

[0117] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, step S46 may include steps S461 to S463:

[0118] Step S461: The flood resilience determination unit of the urban flood resilience simulation model determines the corresponding time-series traffic reliability data based on the city’s time-series flood depth data.

[0119] In this embodiment, the flood resilience determination unit of the urban flood resilience simulation model and the city's time-series flood depth data determined in step S45 determine the city's time-series traffic reliability data. Specifically, this determination is implemented as follows: first, traffic reliability r is defined as the ability of roads, as infrastructure, to maintain normal operation of traffic services during a flood disaster. Then, based on the impact of flooding on the daily lives and transportation of urban residents, a mapping relationship between flood depth and traffic reliability is defined, as shown in Table 2. Based on this mapping relationship and the city's time-series flood depth data determined in step S45, the city's time-series traffic reliability data can be determined.

[0120] Table 2

[0121]

[0122] Step S462: Substituting all independent paths and time sequence traffic reliability data in the city into the city system performance quantification algorithm for calculation, the city system performance curve is determined. The city system performance quantification algorithm expression is: ,in The quantitative index value representing the performance of the urban system at time t; represents an independent path node pair in the urban road network, that is, the shortest path between the i-th node and the j-th node in the road network is an independent path; n is the total number of nodes included in all independent paths; is the node weight; is the path weight; Indicates independent paths The value of traffic reliability at time t; is the number of all independent paths between the i-th node and the j-th node; where the traffic reliability at time t is To form independent paths The reliability of traffic on each road section .

[0123] In this embodiment, after obtaining the time-series traffic reliability data of the city in step S461, the time-series traffic reliability data and all independent path data in the city are substituted into the city system performance quantification algorithm for calculation to obtain the city system performance curve of the city. The expression of the city system performance quantification algorithm is: ,in It represents the quantitative index value of the urban system performance at time t. The urban system performance curve is composed of the lines connecting the quantitative index values of the urban system performance at each time; represents an independent path node pair in the urban road network, that is, the shortest path between the i-th node and the j-th node in the road network; n is the total number of nodes included in all independent paths; is the node weight; is the path weight; Indicates independent paths The value of traffic reliability at time t; is the number of all independent paths between the i-th node and the j-th node; where the traffic reliability at time t is To form independent paths The reliability of traffic on each road section The specific calculation formula can be expressed as .

[0124] Step S463: The ratio of the integral of the urban system performance curve on the time axis from the start of the flood disaster event to the recovery of the urban system to a stable state after the disaster to the integral of the urban system performance curve on the time axis when no flood disaster event occurs is determined as the flood resilience of the city.

[0125] In this embodiment, after obtaining the urban system performance curve for the target adaptive measures distribution under the target rainfall scenario in step S462, the integral of the urban system performance curve over the time axis from the onset of the flood disaster event under the target rainfall scenario to the post-disaster recovery of the urban system to a stable state is compared with the integral of the urban system performance curve over the time axis when no flood disaster event occurred. This yields the value of the city's flood resilience under the target rainfall scenario with the target adaptive measures distribution. The specific calculation expression is: , where R is the flood resilience of the city; is the starting time of the flood disaster event; T is the total time from the starting time of the flood disaster event to the recovery of the urban system to a stable state after the disaster; is the performance level of the system at time t after a flood disaster occurs in the city; It is the performance level of normal system operation at time t when the city is not affected by flood disasters.

[0126] In conjunction with the above embodiments, in one implementation, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, independent paths in an urban road network are determined, including: determining the bifurcation locations of bifurcated roads in an urban road as road nodes, and constructing the two road nodes into node pairs; cyclically searching for a new shortest path among the remaining paths between the node pairs using a target algorithm that does not overlap with the shortest path previously searched; and determining all shortest paths obtained from the search as independent paths to obtain independent paths in the urban road network.

[0127] In this embodiment, the bifurcation locations of forked intersections in an urban road network are determined as road nodes. Then, every two road nodes form a node pair. Then, for each node pair, a target algorithm (preferably a Dijkstra algorithm) is used to search for the shortest path from one road node to another road node in the node pair. This shortest path is determined as an independent path. Then, for this node pair, a search is continued for the shortest path among all remaining paths from one road node to another road node in the node pair. At the same time, this shortest path cannot have any overlapping path segments with the shortest path previously determined to belong to the node pair. Then, this shortest path is also determined as an independent path. This cycle is repeated until no new shortest path that meets the conditions can be found (that is, there is no new shortest path that does not have any overlapping path segments with the previously determined independent path corresponding to the node pair). Then, the independent path corresponding to the node pair is determined, and then the independent paths between new node pairs are determined, thereby obtaining all independent paths in the urban road network.

[0128] In combination with the above embodiments, in one embodiment, the present application also provides an adaptive strategy optimization method based on urban flood resilience. In the adaptive strategy optimization method based on urban flood resilience, step S5 may include steps S51 to S52:

[0129] Step S51: Substitute the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario into the cost-benefit index algorithm for calculation to determine the unit cost flood resilience improvement value of each adaptive strategy type under the target rainfall scenario. The cost-benefit index algorithm expression is: , where I is the degree of improvement in flood resilience per unit cost; The flood resilience of the city after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the flood resilience of cities that have not implemented adaptive strategies under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategies under the target rainfall scenario.

[0130] In this example, the simulation results of urban flood resilience for various adaptive strategy types under the target rainfall scenario are substituted into the cost-benefit index algorithm to calculate and determine the unit cost flood resilience improvement value of each adaptive strategy type under the target rainfall scenario. The cost-benefit index algorithm expression is: , where I is the degree of improvement in flood resilience per unit cost; The flood resilience of the city after implementing the corresponding adaptive strategy type under the target rainfall scenario, = ∘ ...

[0131] Step S52: Determine the adaptive strategy type with the largest flood resilience improvement value under unit cost as the target adaptive strategy type under the target rainfall scenario.

[0132] In this embodiment, the target adaptive measure distribution for the adaptive strategy type with the highest degree of improvement in flood resilience indicates that the target adaptive measure distribution for that adaptive strategy type has the highest degree of improvement in flood resilience per unit cost. Given limited economic costs, the optimal approach is to select the target adaptive measure distribution for that adaptive strategy type as the optimal adaptive strategy. Therefore, this application determines the adaptive strategy type with the highest value of improvement in flood resilience per unit cost as the target adaptive strategy type for the target rainfall scenario. If economic costs are sufficient, the adaptive strategy type with the highest flood resilience is selected as the target adaptive strategy type for the target rainfall scenario.

[0133] In this embodiment, if Figure 4 As shown, Figure 4 The figure shows the performance of the urban system without any adaptive measures under the target rainfall scenario with a return period of 5 years for a specific city. The Res in the figure represents the urban resilience under the target rainfall scenario, at which the urban resilience value is 86.3%. The gray shaded area in the figure represents the urban resilience value without any adaptive measures; and Figure 5 As shown, Figure 5 The figure shows the performance of the urban system under the distribution of target adaptability measures corresponding to the green stormwater street strategy type. The corresponding urban resilience value is 89.1%. The area of the gray shaded part in the figure represents the urban resilience value under the distribution of target adaptability measures corresponding to the green stormwater street strategy type. Figure 5 The LID measures refer to the distribution of target adaptation measures corresponding to the green stormwater street strategy type; and Figure 6 As shown, Figure 6 The figure shows the performance of the urban system under the distribution of target adaptability measures corresponding to the green space expansion strategy type. The corresponding urban resilience value is 88.8%. The area of the gray shaded part in the figure represents the urban resilience value under the distribution of target adaptability measures corresponding to the green space expansion strategy type. Figure 6 The LID measures refer to the distribution of target adaptation measures corresponding to the green space expansion strategy type; and, e.g. Figure 7 As shown, Figure 7 The figure shows the performance of the urban system under the distribution of target adaptability measures corresponding to the green infrastructure integration strategy type. The corresponding urban resilience value is 90.3%. The area of the gray shaded part in the figure represents the urban resilience value under the distribution of target adaptability measures corresponding to the green infrastructure integration strategy type. Figure 7 The LID measures refer to the distribution of target adaptation measures corresponding to the green infrastructure integration strategy type. Figures 4 to 7 It can be found that the urban system performance under the target adaptive measure distribution corresponding to the green infrastructure integration strategy type is the best. If the cost is sufficient, the target adaptive measure distribution corresponding to the green infrastructure integration strategy type can be adopted to improve the city's urban resilience, because this target adaptive measure distribution has the highest impact on urban resilience. If the cost is insufficient, it is necessary to calculate the degree of improvement in urban flood resilience per unit cost for the target adaptive measure distribution under each strategy type to determine which strategy type corresponds to the target adaptive measure distribution that improves urban flood resilience per unit cost (such as strategy type A). If the cost is insufficient, the target adaptive measure distribution corresponding to this strategy type (i.e., strategy type A) should be selected.

[0134] In this embodiment, the present application has the following beneficial effects:

[0135] By constructing an urban flood resilience simulation model, we achieved a dynamic simulation of the flood resilience of urban road-block systems. Using a high-precision digital elevation model (DEM) and detailed urban basic geographic data, we simulated the evolution of flooding in various blocks and roads across the city under different rainfall scenarios. The results included key indicators such as flood depth, flow velocity, inundated area, and duration of waterlogging.

[0136] Dynamic feedback is provided on the actual improvements in flood resilience after implementing the target adaptive measures corresponding to different strategy types. By inputting the target adaptive measures corresponding to different strategy types into the urban flood resilience simulation model, dynamic feedback is provided on the actual effects of implementing the target adaptive measures corresponding to each strategy type under different rainfall scenarios, simulating and evaluating the actual disaster reduction effects of each strategy type. Simultaneously, by analyzing the simulation results, the strengths and weaknesses of each strategy type are identified, providing scientific feedback information. Based on changes in flood scenarios and feedback from strategy implementation, the strategy combination is dynamically adjusted and optimized.

[0137] Providing a reliable basis for selecting flood resilience strategies. Cost-benefit analysis of flood resilience enhancement provides a reliable scientific basis for strategy selection. By quantifying the costs and benefits of each strategy type, calculating the cost-benefit ratio and resilience improvement effect, this provides urban planning and management departments with a scientific basis for decision-making tailored to local conditions.

[0138] It should be noted that for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0139] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0140] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0141] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0142] The above is a detailed introduction to the adaptive strategy optimization method based on urban flood resilience provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. An adaptive strategy optimization method based on urban flood resilience, characterized by: The method comprises: Identify flood risk areas in the city by analyzing the city’s geospatial data; By using the preset constraints of various adaptive measures, the distribution of various adaptive measures in the flood risk area is determined, and the distribution of adaptive measures in the flood risk area is obtained; According to the adaptive strategy type to be evaluated and the target mapping relationship, determining the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution, and obtaining the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area, wherein the target mapping relationship records the correspondence between various adaptive measures and various adaptive strategy types; Inputting the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario; Based on the urban flood resilience simulation results of various adaptation strategy types under the target rainfall scenario, determine the target adaptation strategy type under the target rainfall scenario; The target adaptation measure distribution corresponding to the target adaptation strategy type under the target rainfall scenario is determined as the optimal adaptation strategy under the target rainfall scenario.

2. The adaptive strategy optimization method based on urban flood resilience according to claim 1 is characterized in that: By applying the preset constraints of various adaptive measures, the distribution of various adaptive measures in the flood risk area is determined, and the distribution of adaptive measures in the flood risk area is obtained, including: Determine the initial distribution of various adaptation measures in flood risk areas based on their respective geo-hydrological constraints; The initial distribution is screened according to the user-defined constraints of various adaptive measures to determine the distribution of adaptive measures in the flood risk area.

3. The adaptive strategy optimization method based on urban flood resilience according to claim 1 is characterized in that: According to the mapping relationship between the adaptive strategy type to be evaluated and the target, determining the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated in the adaptive measure distribution, and obtaining the target adaptive measure distribution corresponding to the adaptive strategy type to be evaluated in the flood risk area, including: According to the mapping relationship between the adaptive strategy type to be evaluated and the target, the distribution of various adaptive measures corresponding to the adaptive strategy type to be evaluated is screened from the adaptive measure distribution; The distribution of the various adaptive measures screened out is determined as the target adaptive measure distribution in the flood risk area corresponding to the adaptive strategy type to be evaluated, and the adaptive strategy type includes at least: green stormwater street strategy type, green space expansion strategy type and green infrastructure integration strategy type.

4. The adaptive strategy optimization method based on urban flood resilience according to claim 1 is characterized in that: Construct an urban flood resilience simulation model, including: Constructing a data preprocessing unit for the urban flood resilience simulation model to preprocess the geographic data required to determine urban flood resilience; Construct a rainfall process determination unit for the urban flood resilience simulation model, which is used to determine the rainfall process information under the target rainfall scenario based on the set rainfall intensity formula; Constructing a flood inundation determination unit of the urban flood resilience simulation model, which is used to simulate and analyze the rainfall process information and pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information; Constructing a flood resilience determination unit in the urban flood resilience simulation model to determine the city's flood resilience based on the city's time-series inundation depth data; Based on the constructed data preprocessing unit, rainfall process determination unit, flood inundation determination unit and flood resilience determination unit, an urban flood resilience simulation model is obtained.

5. The adaptive strategy optimization method based on urban flood resilience according to claim 4 is characterized in that: Input the measure parameter information of the target adaptive measure distribution under the target rainfall scenario and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation to determine the urban flood resilience simulation results of the target adaptive measure distribution under the target rainfall scenario, including: Matching and setting various parameters of the surface layer, pavement layer, soil layer, and aquifer of the adaptive measures with different distribution shapes in the target adaptive measure distribution to obtain measure parameter information for adopting the target adaptive measure distribution. The measure parameter information for adopting the target adaptive measure distribution is part of the geographic data required to determine urban flood resilience. Inputting the target adaptive measure distribution parameter information and the target rainfall scenario rainfall data information into the urban flood resilience simulation model for simulation calculation; Preprocessing the geographic data required for determining urban flood resilience by the data preprocessing unit of the urban flood resilience simulation model; The rainfall process determination unit of the urban flood resilience simulation model determines the rainfall process information under the target rainfall scenario based on a set rainfall intensity formula; The flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the rainfall process information and the pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information; The flood resilience determination unit of the urban flood resilience simulation model is used to determine the flood resilience of the city based on the city's time-series inundation depth data.

6. The adaptive strategy optimization method based on urban flood resilience according to claim 5 is characterized in that: The flood inundation determination unit of the urban flood resilience simulation model simulates and calculates the rainfall process information and the pre-processed geographic data based on the storm flood management model to determine the time-series inundation depth data of the city under the rainfall process information, including: The water drop points of the urban pipe network data in the pre-processed geographic data are divided into corresponding sub-catchment areas through a preset algorithm; The average slope and impervious area ratio of the subcatchment were calculated by ArcGIS spatial analysis; Conduct coupled simulation of one-dimensional and two-dimensional hydrodynamic models through the storm flood management model, and set simulation parameters; Based on the rainfall process information, preprocessed geographic data, the average slope and impervious area ratio of the sub-catchment, and taking the sub-catchment as the simulation unit, simulation calculations are performed through the time synchronization and water exchange mechanism of the coupled one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to determine the time-series flooding depth data of the city under the rainfall process information.

7. The adaptive strategy optimization method based on urban flood resilience according to claim 5 is characterized in that: The flood resilience determination unit of the urban flood resilience simulation model determines the city's flood resilience based on the city's time-series inundation depth data, including: The flood resilience determination unit of the urban flood resilience simulation model determines the corresponding time-series traffic reliability data based on the city's time-series flood depth data; The urban system performance curve is determined by substituting all independent paths and time sequence traffic reliability data in the city into the urban system performance quantification algorithm for calculation. The urban system performance quantification algorithm expression is: ,in The quantitative index value representing the performance of the urban system at time t; represents an independent path node pair in the urban road network, that is, the shortest path between the i-th node and the j-th node in the road network is an independent path; n is the total number of nodes included in all independent paths; is the node weight; is the path weight; Indicates independent paths The value of traffic reliability at time t; is the number of all independent paths between the i-th node and the j-th node; where the traffic reliability at time t is To form independent paths The reliability of traffic on each road section The product of The flood resilience of a city is determined by the ratio of the integral of the urban system performance curve on the time axis from the start of the flood disaster to the recovery of the urban system to a stable state after the disaster to the integral of the urban system performance curve on the time axis when no flood disaster occurs.

8. The adaptive strategy optimization method based on urban flood resilience according to claim 7 is characterized in that: Identify independent paths in an urban road network, including: Determine the bifurcation position of a bifurcated road in an urban road as a road node, and construct the two road nodes into a node pair; The target algorithm is used to cyclically search for a new shortest path among the remaining paths between node pairs that does not overlap with the shortest path previously searched; All the shortest paths obtained by searching are determined as independent paths, and independent paths in the urban road network are obtained.

9. The adaptive strategy optimization method based on urban flood resilience according to claim 1, characterized in that: Based on the simulation results of urban flood resilience of various adaptation strategies under the target rainfall scenario, the target adaptation strategy type under the target rainfall scenario was determined, including: By substituting the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario into the cost-benefit index algorithm for calculation, the unit cost flood resilience improvement value of each adaptive strategy type under the target rainfall scenario is determined. The cost-benefit index algorithm expression is: , where I is the degree of improvement in flood resilience per unit cost; The flood resilience of the city after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the flood resilience of cities that do not implement adaptive strategies under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategies under the target rainfall scenario; The adaptive strategy type with the largest improvement value in flood resilience under unit cost is determined as the target adaptive strategy type under the target rainfall scenario.

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