Adaptive strategy optimization method based on urban flood toughness

Abstract

The invention provides an adaptability strategy optimization method based on urban flood toughness, which is applied to the technical field of disaster emergency disposal, and comprises the following steps: determining an urban flood risk area by analyzing geographic space data; determining adaptive measure distribution of the adaptive measures in the flood risk area through preset constraint conditions of the adaptive measures; according to the to-be-evaluated adaptive strategy type and the target mapping relationship, determining distribution of various adaptive measures corresponding to the to-be-evaluated adaptive strategy type in the adaptive measure distribution, and obtaining target adaptive measure distribution; inputting the measure parameter information and the rainfall data information of the target adaptability measure distribution in the target rainfall scene into an urban flood toughness simulation model for simulation, and determining a flood toughness simulation result; and based on the flood toughness simulation result, determining a target adaptability strategy type, and determining the corresponding target adaptability measure distribution as an optimal adaptability strategy. The objective of the invention is to evaluate an optimal adaptability strategy in an urban rainfall scene.

Description

Technical Field

[0001] This application relates to the technical field of disaster emergency response, and particularly to an optimization method for adaptation strategies based on urban flood resilience. Background Art

[0002] In the research on the selection and optimization of flood adaptation strategies, how to select the most effective adaptation strategies, how to evaluate the actual effects of the implemented adaptation strategies, and how to optimize and adjust the adaptation strategies based on the evaluation results are the keys to formulating adaptation strategies. Most of the current research on the selection and optimization of flood adaptation strategies stays at the qualitative analysis level, lacking dynamic feedback based on the rainfall inundation process as the basis for strategy selection, and unable to comprehensively identify and understand the complex dynamic behaviors of urban systems in the face of flood disasters. At the same time, the existing implementation framework for adaptation strategies fails to provide a clear method system or evaluation means to guide the selection and adjustment of strategies in different regions in practice, which limits the effectiveness of adaptation strategies or plans in the long-term development process when dealing with more complex or severe flood scenarios that may occur in the future. Summary of the Invention

[0003] In view of this, this application provides an optimization method for adaptation strategies based on urban flood resilience, aiming to evaluate and determine the adaptation strategies that perform best among various adaptation strategies in the corresponding urban rainfall scenarios, and provide decision-making basis for local conditions for urban planning.

[0004] In the first aspect of the embodiments of this application, an optimization method for adaptation strategies based on urban flood resilience is provided. The method includes: Determining the flood risk areas of the city by analyzing the geographical spatial data of the city; Determining the distribution of various adaptation measures in the flood risk areas through the preset constraint conditions of each adaptation measure, and obtaining the distribution of adaptation measures in the flood risk areas; Determining the distribution of various adaptation measures corresponding to the type of adaptation strategy to be evaluated in the distribution of adaptation measures according to the target mapping relationship between the type of adaptation strategy to be evaluated and the target, and obtaining the target adaptation measure distribution corresponding to the type of adaptation strategy to be evaluated in the flood risk areas, where the target mapping relationship records the corresponding relationship between various adaptation measures and various types of adaptation strategies; Inputting the measure parameter information of taking the target adaptation measure distribution and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation, and determining the urban flood resilience simulation result of taking the target adaptation measure distribution in the target rainfall scenario; Based on the simulation results of urban flood resilience under various types of adaptation strategies in the target rainfall scenario, determine the target type of adaptation strategy in the target rainfall scenario; Determine the distribution of target adaptation measures corresponding to the target type of adaptation strategy in the target rainfall scenario as the optimal adaptation strategy in the target rainfall scenario.

[0005] Optionally, determine the distribution of various adaptation measures in the flood risk area through the respective preset constraints of various adaptation measures, and obtain the distribution of adaptation measures in the flood risk area, including: Determine the initial distribution of various adaptation measures in the flood risk area through the respective geo-hydrological constraints of various adaptation measures; According to the respective custom constraints of various adaptation measures, screen the initial distribution to determine the distribution of adaptation measures in the flood risk area.

[0006] Optionally, according to the target mapping relationship between the adaptation strategy type to be evaluated and the target, determine the distribution of various adaptation measures corresponding to the adaptation strategy type to be evaluated in the distribution of adaptation measures, and obtain the distribution of target adaptation measures corresponding to the adaptation strategy type to be evaluated in the flood risk area, including: According to the target mapping relationship between the adaptation strategy type to be evaluated and the target, screen out the distribution of various adaptation measures corresponding to the adaptation strategy type to be evaluated from the distribution of adaptation measures; Determine the distribution of the screened various adaptation measures as the distribution of target adaptation measures corresponding to the adaptation strategy type to be evaluated in the flood risk area, and the adaptation strategy type at least includes: green stormwater street strategy type, green space expansion strategy type, and green infrastructure integration strategy type.

[0007] Optionally, construct an urban flood resilience simulation model, including: Construct a data preprocessing unit of the urban flood resilience simulation model for preprocessing the geographical data required to determine urban flood resilience; Construct a rainfall process determination unit of the urban flood resilience simulation model for determining the rainfall process information in the target rainfall scenario based on the set rainfall intensity formula; Construct a flood inundation determination unit of the urban flood resilience simulation model for performing simulation analysis on the rainfall process information and the preprocessed geographical data based on the stormwater management model to determine the time-series inundation depth data of the city under the rainfall process information; Construct a flood resilience determination unit of the urban flood resilience simulation model for determining the flood resilience of the city based on the time-series inundation depth data of the city; 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.

[0008] Optionally, input the measure parameter information of the distribution of target adaptation measures 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 result of taking the distribution of the target adaptation measures under the target rainfall scenario, including: Match and set the parameters of the surface layer, road surface layer, soil layer, and water storage layer of the adaptation measures with different shape distributions in the distribution of target adaptation measures to obtain the measure parameter information of taking the distribution of target adaptation measures. The measure parameter information of taking the distribution of target adaptation measures belongs to a part of the geographical data required to determine urban flood resilience; Input the measure parameter information of taking the distribution of target adaptation measures and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation; Preprocess the geographical data required to determine urban flood resilience through the data preprocessing unit of the urban flood resilience simulation model; Through the rainfall process determination unit of the urban flood resilience simulation model, based on the set rainfall intensity formula, determine the rainfall process information under the target rainfall scenario; Through the flood inundation determination unit of the urban flood resilience simulation model, based on the stormwater management model, perform simulation calculations on the rainfall process information and the preprocessed geographical data to determine the temporal inundation water depth data of the city under the rainfall process information; Through the flood resilience determination unit of the urban flood resilience simulation model, based on the temporal inundation water depth data of the city, determine the flood resilience of the city.

[0009] Optionally, through the flood inundation determination unit of the urban flood resilience simulation model, based on the stormwater management model, perform simulation calculations on the rainfall process information and the preprocessed geographical data to determine the temporal inundation water depth data of the city under the rainfall process information, including: Divide the pipe network water dropping points of the urban pipe network data in the preprocessed geographical data into corresponding sub-catchments through a preset algorithm; Calculate the average slope and impervious area ratio of the sub-catchments through ArcGIS spatial analysis; Perform coupled simulation of the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model through the stormwater management model and set the simulation parameters; Based on the rainfall process information, preprocessed geographical data, average slope and impervious area ratio of sub-catchments, taking sub-catchments as simulation units, through the time synchronization and water volume exchange mechanisms of the coupled one-dimensional hydrodynamic model and two-dimensional hydrodynamic model for simulation calculation, to determine the time-series inundation depth data of the city under the rainfall process information.

[0010] Optionally, through the flood resilience determination unit of the urban flood resilience simulation model, based on the time-series inundation depth data of the city, to determine the flood resilience of the city, including: Through the flood resilience determination unit of the urban flood resilience simulation model, based on the time-series inundation depth data of the city, to determine the corresponding time-series traffic reliability data; By substituting all independent paths in the city and the time-series traffic reliability data into the urban system performance quantification algorithm for calculation, to determine the urban system performance curve, and the expression of the urban system performance quantification algorithm is , where represents the quantification index value of the urban system performance at time t; represents the 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; represents the independent path and the traffic reliability value at time t; is the number of all independent paths between the i-th node and the j-th node; among them, the traffic reliability value at time t is the composition of the independent path and the traffic reliability of each section of the road is the product; Taking the ratio of the integral of the urban system performance curve on the time axis during the period from the start time of the flood disaster event to the time when the urban system resumes 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, and determining it as the flood resilience of the city.

[0011] Optionally, to determine the independent paths in the urban road network, including: Determine the bifurcation positions of the bifurcated roads in the urban roads as road nodes, and construct the two road nodes as a node pair; Through the target algorithm, circularly search for new shortest paths in the remaining paths between node pairs that do not coincide with the previously searched shortest paths; Determine all the searched shortest paths as independent paths to obtain the independent paths in the urban road network.

[0012] Optionally, 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, including: By substituting the urban flood resilience simulation results of various adaptation strategy types under the target rainfall scenario into the cost-benefit index algorithm for calculation respectively, determine the flood resilience improvement value per unit cost of each adaptation strategy type under the target rainfall scenario. The expression of the cost-benefit index algorithm is , where I is the degree of flood resilience improvement per unit cost; is the flood resilience of the city after implementing the corresponding adaptation strategy type under the target rainfall scenario, is the flood resilience of the city without implementing the adaptation strategy type under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptation strategy type under the target rainfall scenario; Determine the adaptation strategy type with the largest flood resilience improvement value per unit cost as the target adaptation strategy type under the target rainfall scenario.

[0013] Regarding the prior art, the present application has the following advantages: An optimization method for adaptation strategies based on urban flood resilience provided by an embodiment of the present application. First, by analyzing the geographical spatial data of a city, the flood risk areas of the city are determined; through the preset constraint conditions of various adaptation measures, the distribution of various adaptation measures in the flood risk areas is determined to obtain the distribution of adaptation measures in the flood risk areas; according to the target mapping relationship between the types of adaptation strategies to be evaluated and the targets, the distribution of various adaptation measures corresponding to the types of adaptation strategies to be evaluated in the distribution of adaptation measures is determined to obtain the target distribution of adaptation measures corresponding to the types of adaptation strategies to be evaluated in the flood risk areas, and the target mapping relationship records the corresponding relationship between various adaptation measures and various types of adaptation strategies; the measure parameter information of the measures adopting the target distribution of adaptation measures 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 result of adopting the target distribution of adaptation measures in the target rainfall scenario; based on the urban flood resilience simulation results of various types of adaptation strategies in the target rainfall scenario, the type of target adaptation strategy in the target rainfall scenario is determined; the target distribution of adaptation measures corresponding to the type of target adaptation strategy in the target rainfall scenario is determined as the optimal adaptation strategy in the target rainfall scenario. Thus, the present application first determines the areas in the city where there is a risk of flood disasters, then evaluating the areas can improve the evaluation efficiency, then for the determined flood risk areas, the distribution of various adaptation measures is determined, and then based on the target mapping relationship between various adaptation measures and various types of adaptation strategies, the target distribution of adaptation measures corresponding to the type of adaptation strategy is determined, and then based on the target distribution of adaptation measures, the flood resilience of the city affected by the target distribution of adaptation measures is determined. Based on the different flood resilience performances corresponding to different types of adaptation strategies, the target distribution of adaptation measures corresponding to the type of adaptation strategy with the best performance is selected as the optimal adaptation strategy. By this method, the optimal adaptation strategies of various adaptation strategies in the corresponding urban rainfall scenarios can be evaluated and determined, providing a decision-making basis tailored to local conditions for urban planning.

[0014] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are hereinafter specifically exemplified. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0016] Figure 1Flowchart of an adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application; Figure 2 Schematic diagram of the distribution of various adaptive measures in a flood risk area in an adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application; Figure 3 Another schematic diagram of the distribution of various adaptive measures in a flood risk area in an adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application; Figure 4 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 by an embodiment of the present application; Figure 5 Schematic diagram of the performance of an urban system 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 by an embodiment of the present application; Figure 6 Schematic diagram of the performance of an urban system under the distribution of target adaptive measures corresponding to the green space expansion strategy type in an adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application; Figure 7 Schematic diagram of the performance of an urban system 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 by an embodiment of the present application. Detailed implementation manners

[0017] Hereinafter, exemplary embodiments of the present application will be described in more detail with reference to the accompanying drawings.

[0018] Figure 1 Flowchart of an adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application, as Figure 1 shown, the method includes: Step S1: Determine the flood risk area of the city by analyzing the geographical spatial data of the city.

[0019] In this embodiment, in order to improve the evaluation efficiency of the adaptation strategies for cities, this application determines the distribution of adaptation measures in the flood risk areas of cities and conducts subsequent evaluations. Specifically, geospatial data of the city is obtained, which at least includes land use data, water system distribution data, road status data, traffic flow data, and public facility distribution data. By analyzing the geospatial data of the city, potential flood risk areas in the city are determined. These potential risk areas are areas in the city where there is a greater risk of flooding or areas that will be severely affected by flooding. For example, analyze the spatial distribution of various land use types and their relationship with flood risk, identify potential flood risk points, and determine a certain range including the potential flood risk points as flood risk areas; evaluate the traffic capacity of the road network and its weak links in flood disasters, and determine a certain range including the weak links as flood risk areas; identify the spatial distribution of population activities and key protection areas and determine them as flood risk areas. Among them, there may be multiple flood risk areas in a single city.

[0020] Step S2: Determine the distribution of various adaptation measures in the flood risk areas through the preset constraint conditions of each adaptation measure, and obtain the distribution of adaptation measures in the flood risk areas.

[0021] In this embodiment, corresponding preset constraint conditions are set in advance for each adaptation measure. Then, after determining the flood risk areas in the city through Step S1, according to the preset constraint conditions of each adaptation measure, determine which locations in the flood risk areas various adaptation measures can be implemented, so as to obtain the distribution of various adaptation measures in the flood risk areas of the city. This distribution is the distribution of adaptation measures in the flood risk areas of the city, as Figure 2 shown, Figure 2 exemplarily shows the flood risk areas in a city and the distribution locations of various adaptation measures in these flood risk areas. The obtained result of this distribution location is the distribution of adaptation measures in the flood risk areas of the city. It should be understood that in the case of ignoring the evaluation efficiency and considering the evaluation of the entire city, determining the distribution of various adaptation measures can also be determined within the scope of the entire city, and the corresponding subsequent evaluation is adjusted to evaluate the adaptation strategy for the entire city.

[0022] Step S3: According to the target mapping relationship between the adaptation strategy type to be evaluated and the adaptation measures, determine the distribution of various adaptation measures corresponding to the adaptation strategy type to be evaluated in the distribution of the adaptation measures, and obtain the target adaptation measure distribution corresponding to the adaptation strategy type to be evaluated in the flood risk areas. The target mapping relationship records the corresponding relationship between various adaptation measures and various adaptation strategy types.

[0023] In the present application, step S3 specifically includes: according to the type of the adaptability strategy to be evaluated and the target mapping relationship, screening out the distributions of various adaptability measures corresponding to the type of the adaptability strategy to be evaluated from the distribution of the adaptability measures; and determining the distributions of the screened various adaptability measures as the target adaptability measure distributions corresponding to the type of the adaptability strategy to be evaluated in the flood risk area, where the type of the adaptability strategy at least includes: the green stormwater street strategy type, the green space expansion strategy type, and the green infrastructure integration strategy type.

[0024] In this embodiment, the present application predefines multiple adaptability strategies. For different types of adaptability strategies, there will also be differences in the types of adaptability measures that can be taken. That is, which types of adaptability measures can be adopted under a certain type of adaptability strategy are predefined. Among them, the multiple adaptability strategies predefined in the present application include the green stormwater street strategy type, the green space expansion strategy type, and the green infrastructure integration strategy type. The adaptability measures that can be taken under the green stormwater street strategy type are all adaptability measures related to the street network, such as permeable paving, street greening, and rain gardens. The adaptability measures that can be taken under the green space expansion strategy type are the types of adaptability measures that can be taken in the built road and block spaces where it is not easy to increase green spaces and water bodies on a large scale, such as green roofs, rain gardens, etc. The adaptability measures that can be taken under the green infrastructure integration strategy type are all types of adaptability measures.

[0025] It should be understood that the multiple adaptability strategies can also be other adaptability strategies, but for each type of adaptability strategy, there are corresponding adaptability measures that can be taken under that type of adaptability strategy.

[0026] In this embodiment, the present application pre - establishes the corresponding relationship between various types of adaptability strategies and various adaptability measures, and this corresponding relationship is the target mapping relationship. For the current type of adaptability strategy to be evaluated, through the mapping relationship between various types of adaptability strategies and various adaptability measures recorded in the target mapping relationship, determine the various adaptability measures corresponding to the type of adaptability strategy to be evaluated, and then, for the total distribution of adaptability measures obtained through step S2, only retain the various adaptability measures corresponding to the type of adaptability strategy to be evaluated in this distribution of adaptability measures, so as to obtain the target adaptability measure distribution corresponding to the type of adaptability strategy to be evaluated in the flood risk area. For example, as Figure 2 shown, when the various adaptability measures corresponding to the type of adaptability strategy A to be evaluated recorded in the target mapping relationship include the a1 - type adaptability measures and the a2 - type adaptability measures, based on the determined as Figure 2The distribution of the total adaptation measures in the flood risk areas of the shown city. Only retaining the adaptation measure distribution obtained by the a1 type of adaptation measures and the a2 type of adaptation measures corresponding to the to-be-evaluated adaptation strategy type A in the total adaptation measure distribution is the adaptation measure distribution corresponding to the to-be-evaluated adaptation strategy type A in the flood risk areas of the city. For example, Figure 3 The shown is the obtained adaptation measure distribution corresponding to the to-be-evaluated adaptation strategy type A. For each to-be-evaluated adaptation strategy type, its corresponding target adaptation measure distribution can be determined through the same implementation method as in step S3.

[0027] Step S4: Input the measure parameter information of the target adaptation measure distribution taken 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 result when taking the target adaptation measure distribution under the target rainfall scenario.

[0028] In this embodiment, the target rainfall scenario is the scenario of a rainstorm flood event with a specific annual recurrence period. This specific annual recurrence period can be any annual recurrence period, such as the scenario of a rainstorm flood event with a 5-year recurrence period, the scenario of a rainstorm flood event with a 20-year recurrence period, the scenario of a rainstorm flood event with a 50-year recurrence period, the scenario of a rainstorm flood event with a 100-year recurrence period. The recurrence period refers to the average number of years for such a large flood disaster event to occur once.

[0029] In this embodiment, this application has pre-constructed an urban flood resilience simulation model. By inputting the rainfall data information (this rainfall data information is the rainfall intensity per minute, with the unit of mm / min) under the target rainfall scenario of the city, and the measure parameter information corresponding to the target adaptation measure distribution taken in the flood risk areas of the city (this target adaptation measure distribution is determined through step S3) into the urban flood resilience simulation model for simulation calculation, the flood resilience value of the city is obtained. This flood resilience value represents the urban resilience performance of the city after taking the target adaptation measure distribution under the influence of this target rainfall scenario. The larger its value, the better the urban resilience performance, indicating that the performance of the city in coping with flood disasters has been effectively improved. Specifically, input the rainfall data information under the target rainfall scenario and the measure parameter information corresponding to the target adaptation measure distribution under the to-be-evaluated adaptation strategy type into the pre-constructed urban flood resilience simulation model for simulation calculation to obtain the flood resilience value of the city. This flood resilience value is the corresponding urban flood resilience simulation result. Thus, under the target rainfall scenario, for the target adaptation measure distribution under each to-be-evaluated adaptation strategy type, the respective corresponding flood resilience values can be obtained through the same implementation method as in step S4 for simulation calculation.

[0030] 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.

[0031] In this embodiment, after calculating the urban flood resilience simulation results of each adaptive strategy type under the target rainfall scenario through step S4, an optional implementation method is to determine the adaptive strategy type with the maximum flood resilience value of the simulated city as the target adaptive strategy type under the target rainfall scenario.

[0032] Step S6: Determine 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.

[0033] In this embodiment, after determining the target adaptive strategy type under the target rainfall scenario through step S5, the target adaptive measure distribution corresponding to the target adaptive strategy type is determined as the optimal adaptive strategy under the target rainfall scenario. This optimal adaptive strategy is actually which positions in the flood risk areas of the city record the various adaptive measures set in the target adaptive measure distribution.

[0034] An adaptive strategy optimization method based on urban flood resilience provided by an embodiment of the present application. First, by analyzing the geographical spatial data of the city, the flood risk areas of the city are determined; through the preset constraint conditions of various adaptive measures, the distribution of various adaptive measures in the flood risk areas is determined, and the distribution of adaptive measures in the flood risk areas is obtained; according to the target mapping relationship between the type of adaptive strategy to be evaluated and the target, the distribution of various adaptive measures corresponding to the type of adaptive strategy to be evaluated in the distribution of adaptive measures is determined, and the target adaptive measure distribution corresponding to the type of adaptive strategy to be evaluated in the flood risk areas is obtained. The target mapping relationship records the corresponding relationship between various adaptive measures and various types of adaptive strategies; the measure parameter information of the target adaptive measure distribution 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 result of adopting the target adaptive measure distribution in the target rainfall scenario; based on the urban flood resilience simulation results of various types of adaptive strategies in the target rainfall scenario, the type of target adaptive strategy in the target rainfall scenario is determined; the target adaptive measure distribution corresponding to the type of target adaptive strategy in the target rainfall scenario is determined as the optimal adaptive strategy in the target rainfall scenario. Thus, the present application first determines the areas in the city where there is a risk of flood disasters, then evaluates the areas to improve the evaluation efficiency, then determines the distribution of various adaptive measures for the determined flood risk areas, and then based on the target mapping relationship between various adaptive measures and various types of adaptive strategies, determines the target adaptive measure distribution corresponding to the type of adaptive strategy, and then based on the target adaptive measure distribution, determines the flood resilience of the city under the influence of the target adaptive measure distribution. Based on the different flood resilience performances corresponding to different types of adaptive strategies, the target adaptive measure distribution corresponding to the type of adaptive strategy with the best performance is selected as the optimal adaptive strategy. By this method, it is possible to evaluate and determine the adaptive strategy with the best performance among various adaptive strategies in the corresponding urban rainfall scenario, providing a decision-making basis tailored to local conditions for urban planning.

[0035] Combined with the above embodiments, in one implementation manner, the embodiment of the present application further provides an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, step S2 may include steps S21 to S22: Step S21: Determine the initial distribution of various adaptive measures in the flood risk areas through the respective geographical and hydrological constraints of various adaptive measures.

[0036] In this embodiment, the basic data of the urban flood risk area is loaded into the BMP site selection tool in the required format. By setting the respective geo-hydrological constraints of various adaptation measures, spatial suitability allocation of various adaptation measures is carried out in the urban flood risk area, so as to obtain the initial distribution of these adaptation measures in space. Among them, 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.

[0037] Step S22: According to the respective custom constraints of various adaptation measures, screen the initial distribution to determine the distribution of adaptation measures in the flood risk area.

[0038] In this embodiment, after obtaining the initial distribution of various adaptation measures in the urban flood risk area through step S21, for each adaptation measure, specific values on various custom constraint types are set. Then, using the spatial analysis function of ArcGIS, based on the respective custom constraints of various adaptation measures, each adaptation measure that conforms to its corresponding custom constraint in the initial distribution is screened out. Then, the final distribution of adaptation measures in the urban flood risk area is composed of all the adaptation measures screened out from the initial distribution. Among them, the various custom constraint types include but are not limited to single area, single width, and land use nature. As shown in Table 1, Table 1 shows the specific geo-hydrological constraints and specific custom constraints corresponding to some adaptation measures. It should be understood that some adaptation measures do not involve all or part of the geo-hydrological constraints.

[0039] Table 1

[0040] Combined with the above embodiments, in one implementation, the embodiment of the present application also provides an optimization method for adaptation strategies based on urban flood resilience. In this optimization method for adaptation strategies 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: Step S011: Construct a data preprocessing unit of the urban flood resilience simulation model for preprocessing the geographical data required to determine urban flood resilience.

[0041] In this embodiment, a data preprocessing unit for constructing a simulation model of urban flood resilience is built. This data preprocessing unit is used to preprocess various geographical data required to determine urban flood resilience, and obtain the preprocessed various geographical data. The preprocessing at least includes coordinate system conversion and cropping of various geographical data to unify the coordinate systems of various geographical data and obtain various geographical data under the flood risk area of the city to be evaluated with a unified coordinate system; raising the building DEM data; generalizing the drainage pipe network; and checking and confirming the topological relationship of the drainage pipe network. Among them, the various geographical data at least include rainfall data, DEM data, drainage pipe network data, land use types, road network data, underlying surface data, and river channel data.

[0042] Step S012: 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.

[0043] In this embodiment, a rainfall process determination unit for the urban flood resilience simulation model is built. This rainfall process determination unit is used to calculate and determine the rainfall process information under the target rainfall scenario based on the set rainfall intensity formula. The rainfall process information under the target rainfall scenario refers to the magnitude of the rainfall in the city under this target rainfall scenario and its distribution in time and space.

[0044] In this embodiment, the set rainfall intensity formula is , where i is the rainfall intensity, with the unit of mm / min; t is the rainfall duration, with the unit of min; and T is the recurrence period, with the unit of year.

[0045] Step S013: Construct a flood inundation determination unit for the urban flood resilience simulation model, which is used to perform simulation analysis on the rainfall process information and the preprocessed geographical data based on the Storm Water Management Model, and determine the temporal inundation depth data of the city under the rainfall process information.

[0046] In this embodiment, a flood inundation determination unit for the urban flood resilience simulation model is built. This flood inundation determination unit is used to perform simulation analysis on the rainfall process information under the target rainfall scenario and the geographical data preprocessed by the data preprocessing unit through the Storm Water Management Model, and determine the temporal inundation depth data of the city corresponding to the rainfall process information. The temporal inundation depth data refers to the inundation depth at each moment of the city within a continuous time period under this rainfall process information.

[0047] Step S014: Construct a flood resilience determination unit for the urban flood resilience simulation model, which is used to determine the flood resilience of the city based on the temporal inundation depth data of the city.

[0048] In this embodiment, a flood resilience determination unit for constructing a urban flood resilience simulation model is provided. The flood resilience determination unit is configured to determine the urban flood resilience based on the time-series inundation depth data of the city under the rainfall process information corresponding to the determined target rainfall scenario.

[0049] Step S015: Based on the constructed data preprocessing unit, rainfall process determination unit, flood inundation determination unit, and flood resilience determination unit, obtain the urban flood resilience simulation model.

[0050] 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 the urban flood resilience.

[0051] Combined with the above embodiments, in one implementation manner, the embodiments of the present application further provide an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, step S4 may include steps S41 to S46: Step S41: Match and set the parameters of the surface layer, road surface layer, soil layer, and water storage layer of the adaptive measures with different shape distributions in the target adaptive measure distribution to obtain the measure parameter information of adopting the target adaptive measure distribution. The measure parameter information of adopting the target adaptive measure distribution belongs to a part of the geographical data required for determining the urban flood resilience.

[0052] In this embodiment, match and set the parameters of the surface layer, road surface layer, soil layer, and water storage layer of various point-like, line-like, and area-like adaptive measures in the determined target adaptive measure distribution to obtain the measure parameter information of adopting this target adaptive measure distribution. Among them, the measure parameter information of adopting the target adaptive measure distribution also belongs to a part of the geographical data required for determining the urban flood resilience.

[0053] Step S42: Input the measure parameter information of adopting 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.

[0054] In this embodiment, input the measure parameter information of adopting the target adaptive measure distribution, the rainfall data information under the target rainfall scenario, and the various geographical data required for determining the urban flood resilience into the urban flood resilience simulation model for simulation calculation.

[0055] Step S43: Preprocess the geographical data required for determining the urban flood resilience through the data preprocessing unit of the urban flood resilience simulation model.

[0056] In this embodiment, for each piece of geographical data input into the urban flood resilience simulation model (each piece of geographical data also includes the measure parameter information of the distribution of target adaptation measures), the data preprocessing unit of the urban flood resilience simulation model preprocesses each piece of geographical data to obtain the preprocessed geographical data.

[0057] Step S44: Through the rainfall process determination unit of the urban flood resilience simulation model, based on the set rainfall intensity formula, determine the rainfall process information under the target rainfall scenario.

[0058] In this embodiment, through the rainfall process determination unit of the urban flood resilience simulation model, based on the set rainfall intensity formula, calculate and determine the rainfall process information under the target rainfall scenario.

[0059] Step S45: Through the flood inundation determination unit of the urban flood resilience simulation model, based on the stormwater management model, perform simulation calculations on the rainfall process information and the preprocessed geographical data to determine the sequential inundation depth data of the city under the rainfall process information.

[0060] In this embodiment, the flood inundation determination unit of the urban flood resilience simulation model performs simulation calculations on the preprocessed geographical data obtained through step S43 and the rainfall process information obtained through step S44 through the stormwater management model to obtain the sequential inundation depth data of the city corresponding to the rainfall process information.

[0061] Step S46: Through the flood resilience determination unit of the urban flood resilience simulation model, based on the sequential inundation depth data of the city, determine the flood resilience of the city.

[0062] In this embodiment, the flood resilience determination unit of the urban flood resilience simulation model analyzes and calculates the sequential inundation depth data of the city to determine the flood resilience value of the city.

[0063] Combined with the above embodiments, in one implementation manner, the embodiment of 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, step S45 may include steps S451 to S454: Step S451: Divide the pipe network water dropping points in the urban pipe network data in the preprocessed geographical data into corresponding sub-catchments through a preset algorithm.

[0064] In this embodiment, each pipe network water dropping point in the urban pipe network data in the preprocessed geographical data is used as a node, and the sub-catchments are divided according to the distribution of the nodes through the Thiessen polygon algorithm, a preset algorithm, and then the obtained sub-catchments are manually adjusted to obtain the final sub-catchment division result.

[0065] Step S452: Calculate the average slope and the proportion of impervious area of the sub-catchments through ArcGIS spatial analysis.

[0066] In this embodiment, based on the sub-catchment division result obtained in step S451, calculate the average slope and the proportion of impervious area of each sub-catchment through ArcGIS spatial analysis.

[0067] Step S453: Conduct coupled simulation of the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model through the stormwater management model, and set the simulation parameters.

[0068] In this embodiment, conduct coupled simulation of the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model through the stormwater management model (SWMM Storm Water Management Model), and set the simulation parameters during the simulation process. The simulation parameters at least include: total duration, time step, Manning roughness coefficient. Among them, the one-dimensional hydrodynamic model is a model used to simulate the flow characteristics of water in one-dimensional spaces such as pipes and channels in the urban drainage system; the two-dimensional hydrodynamic model is a model used to simulate and predict hydrodynamic characteristics such as water flow movement, water level change, and flow velocity distribution.

[0069] Step S454: Based on the rainfall process information, the preprocessed geographical data, the average slope and the proportion of impervious area of the sub-catchments, taking the sub-catchments as the simulation unit, conduct simulation calculations through the time synchronization and water volume exchange mechanisms of the coupled one-dimensional hydrodynamic model and two-dimensional hydrodynamic model, and determine the sequential inundation depth data of the city under the rainfall process information.

[0070] In this embodiment, based on the rainfall process information obtained through step S44, the preprocessed geographical data obtained through step S43, and the average slope and the proportion of impervious area of each sub-catchment obtained through step S452, taking the sub-catchments as the simulation unit, conduct simulation calculations of the inundation depth through the time synchronization and water volume exchange mechanisms of the coupled one-dimensional hydrodynamic model and two-dimensional hydrodynamic model, and determine the sequential inundation depth data of the city corresponding to the rainfall process information.

[0071] Combined with the above embodiments, in one implementation manner, the embodiment of the present application further provides an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, step S46 may include steps S461 to S463: Step S461: Through the flood resilience determination unit of the urban flood resilience simulation model, determine the corresponding sequential traffic reliability data based on the sequential inundation depth data of the city.

[0072] In this embodiment, the time-series traffic reliability data of the city is determined by the flood resilience determination unit of the urban flood resilience simulation model and the time-series inundation depth data of the city determined through step S45. The specific determination implementation method is as follows: First, define the traffic reliability r as the ability of the road to maintain normal operation of the traffic service function as an infrastructure during the flood disaster; then, according to the impact of the inundation depth caused by the waterlogging on the daily life and traffic of urban residents, define the mapping relationship between the corresponding inundation depth and the traffic reliability, as shown in Table 2, which shows the mapping relationship between the inundation depth and the traffic reliability. Based on this mapping relationship and the time-series inundation depth data of the city determined through step S45, the time-series traffic reliability data of the city can be determined.

[0073] Table 2

[0074] Step S462: By substituting all the independent paths in the city and the time-series traffic reliability data into the urban system performance quantification algorithm for calculation, determine the urban system performance curve. The expression of the urban system performance quantification algorithm is , where represents the value of the quantification index of the urban system performance at time t; represents the 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; represents the independent path at the traffic reliability value at time t; is the number of all independent paths between the i-th node and the j-th node; among them, the traffic reliability value at time t is the traffic reliability of each section that makes up the independent path .

[0075] In this embodiment, after obtaining the time-series traffic reliability data of the city through step S461, substitute this time-series traffic reliability data and all the independent path data in the city into the urban system performance quantification algorithm for calculation to obtain the urban system performance curve of the city. The expression of this urban system performance quantification algorithm is , where represents the value of the quantification index of the urban system performance at time t, and the urban system performance curve is composed of connecting the values of the quantification indexes of the urban system performance at each moment; ​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; Represents an independent path The value of the traffic reliability at time t; Is the number of all independent paths between the i-th node and the j-th node; among them, the value of the traffic reliability at time t Is the component of the independent path The traffic reliability of each section of the road The product of, and the specific calculation formula can be expressed as .

[0076] Step S463: Determine the flood resilience of the city by taking the ratio of the integral of the urban system performance curve on the time axis during the period from the start time of the flood disaster event to the time when the post-disaster urban system returns to a stable state to the integral of the urban system performance curve on the time axis when no flood disaster event occurs.

[0077] In this embodiment, after obtaining the urban system performance curve with the distribution of target adaptation measures under the target rainfall scenario through step S462, compare the integral of this urban system performance curve on the time axis during the period from the start time of the flood disaster event to the time when the post-disaster urban system returns to a stable state under the target rainfall scenario with the integral of the urban system performance curve on the time axis when no flood disaster event occurs, to obtain the value of the flood resilience of the city under the condition of the distribution of target adaptation measures under the target rainfall scenario. The specific calculation expression is: , where R is the flood resilience of the city; Is the start time of the flood disaster event; T is the total duration from the start time of the flood disaster event to the time when the post-disaster urban system returns to a stable state; Is the performance level of the system at time t after the city has a flood disaster event; Is the normal operation performance level of the system at time t when the city has no flood disaster event.

[0078] Combined with the above embodiments, in one implementation manner, the embodiments of the present application also provide an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, determining the independent paths in the urban road network includes: determining the bifurcation positions of the bifurcated roads in the urban roads as road nodes, and constructing two road nodes into a node pair; circularly searching for new shortest paths that do not coincide with the previously searched shortest paths in the remaining paths between the node pairs through a target algorithm; and determining all the searched shortest paths as independent paths to obtain the independent paths in the urban road network.

[0079] In this embodiment, the bifurcation position of the bifurcation intersection in the urban road network is determined as a road node, and then every two road nodes form a node pair. Then, for each node pair, the shortest path from one road node in the node pair to the other road node is searched through a target algorithm (preferably the Dijkstra algorithm), and this shortest path is determined as an independent path. Then, for this node pair, continue to search for the shortest path among all the remaining paths from one road node in the node pair to the other road node, and at the same time, this shortest path cannot have overlapping path segments with the shortest path determined for this node pair previously searched. Then, this shortest path is also determined as an independent path. Repeat this process until no new shortest path that meets the conditions can be searched (that is, there is no new shortest path that does not have overlapping path segments with the independent paths corresponding to this node pair determined previously), then the determination of the independent paths corresponding to this node pair is completed. Furthermore, determine the independent paths between new node pairs, so as to obtain all the independent paths in the urban road network.

[0080] Combined with the above embodiments, in one implementation manner, the embodiments of the present application also provide an adaptive strategy optimization method based on urban flood resilience. In this adaptive strategy optimization method based on urban flood resilience, step S5 may include steps S51 to S52: Step S51: 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, determine the urban flood resilience improvement value per unit cost of each adaptive strategy type under the target rainfall scenario. The expression of the cost-benefit index algorithm is , where I is the degree of improvement in flood resilience per unit cost; is the urban flood resilience after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the urban flood resilience without implementing the adaptive strategy type under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategy type under the target rainfall scenario.

[0081] In this embodiment, the urban flood resilience simulation results of various adaptive strategy types under the target rainfall scenario are respectively substituted into the cost-benefit index algorithm for calculation to determine the urban flood resilience improvement value per unit cost of each adaptive strategy type under the target rainfall scenario. Among them, the expression of the cost-benefit index algorithm is , where I is the degree of improvement in flood resilience per unit cost; is the urban flood resilience after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the flood resilience of cities without implementing adaptive strategy types under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategy type under the target rainfall scenario. Through this cost-benefit index algorithm, the degree of flood resilience improvement under the target adaptive measure distribution of various adaptive strategy types can be calculated and obtained.

[0082] Step S52: Determine the adaptive strategy type with the largest flood resilience improvement value per unit cost as the target adaptive strategy type under the target rainfall scenario.

[0083] In this embodiment, for the target adaptive measure distribution of the adaptive strategy type with a higher degree of flood resilience improvement, it means that the target adaptive measure distribution of this adaptive strategy type per unit cost has the highest degree of flood resilience improvement. In the case of limited economic costs, the optimal way is to select the target adaptive measure distribution of this adaptive strategy type as the optimal adaptive strategy. Therefore, this application determines the adaptive strategy type with the largest flood resilience improvement value per unit cost as the target adaptive strategy type under the target rainfall scenario. If the economic costs are sufficient, then select the adaptive strategy type with the largest flood resilience as the target adaptive strategy type under the target rainfall scenario.

[0084] In this embodiment, as Figure 4 shown, Figure 4 shows the performance of the urban system without taking any adaptive measures under the target rainfall scenario with a 5-year return period in a specific city. Res in the figure represents the urban resilience under this target rainfall scenario. At this time, the urban resilience value is 86.3%. The area of the gray shaded part in the figure represents the urban resilience value without taking any adaptive measures; and, as Figure 5 shown, Figure 5 shows the performance of the urban system under the target adaptive measure distribution 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 target adaptive measure distribution corresponding to the green stormwater street strategy type. In Figure 5 the LID measures refer to the target adaptive measure distribution corresponding to the green stormwater street strategy type; and, as Figure 6 shown, Figure 6 shows the performance of the urban system under the target adaptive measure distribution 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 target adaptive measure distribution corresponding to the green space expansion strategy type. In Figure 6 the LID measures refer to the target adaptive measure distribution corresponding to the green space expansion strategy type; and, as Figure 7 shown,Figure 7 shows the performance of the urban system under the distribution of target adaptation 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 adaptation measures corresponding to the green infrastructure integration strategy type. In Figure 7 , the LID measures refer to the distribution of target adaptation measures corresponding to the green infrastructure integration strategy type. From Figures 4 to 7 , it can be found that the performance of the urban system is the best under the distribution of target adaptation measures corresponding to the green infrastructure integration strategy type. When the cost is sufficient, the distribution of target adaptation measures corresponding to the green infrastructure integration strategy type can be adopted to improve the urban resilience of the city, because this distribution of target adaptation measures has the highest improvement on urban resilience. When the cost is insufficient, it is necessary to calculate the degree of improvement of urban flood resilience per unit cost for the distribution of target adaptation measures under each strategy type, and determine which distribution of target adaptation measures corresponding to the strategy type has the highest improvement on urban flood resilience per unit cost (such as strategy type A). When the cost is insufficient, it is necessary to select the distribution of target adaptation measures corresponding to this strategy type (i.e., strategy type A).

[0085] In this embodiment, the present application has the following beneficial effects: By constructing an urban flood resilience simulation model, the dynamic simulation of the flood resilience of the urban road-block system is realized. Using a high-precision digital elevation model (DEM) and detailed urban basic geographical data, the flood evolution process of each block and road in the city under different rainfall scenarios is simulated, and the results include key indicators such as flood depth, flow velocity, inundation area, and waterlogging time.

[0086] Dynamically feedback the actual improvement effect of flood resilience after implementing the distribution of target adaptation measures corresponding to different strategy types. By inputting the distribution of target adaptation measures corresponding to different strategy types into the urban flood resilience simulation model, the actual effects after implementing the distribution of target adaptation measures corresponding to each strategy type under different rainfall scenarios can be dynamically feedback, and the actual disaster reduction effects of each strategy type can be simulated and evaluated. At the same time, by analyzing the simulation results, the advantages and disadvantages of the strategy types are identified, scientific feedback information is provided, and the strategy combination is dynamically adjusted and optimized according to the changes in the flood scenario and the feedback after the strategy implementation.

[0087] Provide a reliable basis for the selection of flood resilience improvement strategies. Based on the cost-benefit analysis of flood resilience improvement, a reliable scientific basis for strategy selection is provided. By quantitatively analyzing the costs and benefits of each strategy type, calculating the cost-benefit ratio and the resilience improvement effect, a scientific decision-making basis adapted to local conditions is provided for urban planning and management departments.

[0088] It should be noted that, for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present application are not limited by the described action sequences, because according to the embodiments of the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential for the embodiments of the present application.

[0089] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0090] Those skilled in the art should understand 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 take the form of completely hardware embodiments, completely software embodiments, or embodiments combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0091] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0092] The above has introduced in detail a method for optimizing an adaptive strategy based on urban flood resilience provided by the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An optimization method for adaptive strategies based on urban flood resilience, characterized in that, The method includes: Determining the flood risk areas of the city by analyzing the geospatial data of the city; Determining the distribution of various adaptation measures in the flood risk areas through the preset constraint conditions of the respective adaptation measures, and obtaining the distribution of adaptation measures in the flood risk areas; Determining the distribution of various adaptation measures corresponding to the type of adaptation strategy to be evaluated in the distribution of the adaptation measures according to the target mapping relationship between the type of adaptation strategy to be evaluated and the target, and obtaining the target distribution of adaptation measures corresponding to the type of adaptation strategy to be evaluated in the flood risk areas, where the target mapping relationship records the corresponding relationship between various adaptation measures and various types of adaptation strategies; Inputting the measure parameter information of the target distribution of adaptation measures and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation, and determining the urban flood resilience simulation result of adopting the target distribution of adaptation measures in the target rainfall scenario; Determining the type of target adaptation strategy in the target rainfall scenario based on the urban flood resilience simulation results of various types of adaptation strategies in the target rainfall scenario; Determining the target distribution of adaptation measures corresponding to the type of target adaptation strategy in the target rainfall scenario as the optimal adaptation strategy in the target rainfall scenario.

2. The adaptive strategy optimization method based on urban flood resilience according to claim 1, wherein Determining the distribution of various adaptation measures in the flood risk areas through the preset constraint conditions of the respective adaptation measures, and obtaining the distribution of adaptation measures in the flood risk areas, including: Determining the initial distribution of various adaptation measures in the flood risk areas through the respective geohydrological constraints of the various adaptation measures; Screening the initial distribution according to the respective custom constraints of the various adaptation measures to determine and obtain the distribution of adaptation measures in the flood risk areas.

3. An optimization method for an adaptive strategy based on urban flood resilience according to claim 1, characterized in that Determining the distribution of various adaptation measures corresponding to the type of adaptation strategy to be evaluated in the distribution of the adaptation measures according to the target mapping relationship between the type of adaptation strategy to be evaluated and the target, and obtaining the target distribution of adaptation measures corresponding to the type of adaptation strategy to be evaluated in the flood risk areas, including: Screening out the distribution of various adaptation measures corresponding to the type of adaptation strategy to be evaluated from the distribution of the adaptation measures according to the target mapping relationship between the type of adaptation strategy to be evaluated and the target; Determining the screened distribution of various adaptation measures as the target distribution of adaptation measures corresponding to the type of adaptation strategy to be evaluated in the flood risk areas, where the type of adaptation strategy at least includes: the green rainwater street strategy type, the green space expansion strategy type, and the green infrastructure integration strategy type.

4. The adaptive strategy optimization method based on urban flood resilience according to claim 1, characterized in that Constructing an urban flood resilience simulation model, including: Constructing a data preprocessing unit of the urban flood resilience simulation model for preprocessing the geographical data required to determine urban flood resilience; Constructing a rainfall process determination unit of the urban flood resilience simulation model for determining the rainfall process information in the target rainfall scenario based on the set rainfall intensity formula; A flood inundation determination unit for constructing an urban flood resilience simulation model, which is used to perform simulation analysis on the rainfall process information and preprocessed geographical data based on a stormwater management model, and determine the temporal inundation depth data of the city under the rainfall process information; A flood resilience determination unit for constructing an urban flood resilience simulation model, which is used to determine the flood resilience of the city based on the temporal inundation depth data of the city; An urban flood resilience simulation model is obtained based on the constructed data preprocessing unit, rainfall process determination unit, flood inundation determination unit and flood resilience determination unit.

5. The adaptive strategy optimization method based on urban flood resilience according to claim 4, characterized in that, Input the measure parameter information of the distribution of target adaptation measures and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation, and determine the urban flood resilience simulation result of the distribution of the target adaptation measures under the target rainfall scenario, including: Match and set the parameters of the surface layer, road surface layer, soil layer and water storage layer of the adaptation measures with different shape distributions in the distribution of target adaptation measures to obtain the measure parameter information of the distribution of target adaptation measures, and the measure parameter information of the distribution of target adaptation measures belongs to a part of the geographical data required to determine the urban flood resilience; Input the measure parameter information of the distribution of target adaptation measures and the rainfall data information of the target rainfall scenario into the urban flood resilience simulation model for simulation calculation; Preprocess the geographical data required to determine the urban flood resilience through the data preprocessing unit of the urban flood resilience simulation model; Determine the rainfall process information under the target rainfall scenario through the rainfall process determination unit of the urban flood resilience simulation model based on the set rainfall intensity formula; Through the flood inundation determination unit of the urban flood resilience simulation model, perform simulation calculation on the rainfall process information and the preprocessed geographical data based on the stormwater management model, and determine the temporal inundation depth data of the city under the rainfall process information; Through the flood resilience determination unit of the urban flood resilience simulation model, determine the flood resilience of the city based on the temporal inundation depth data of the city.

6. The adaptive strategy optimization method based on urban flood resilience according to claim 5, characterized in that, Through the flood inundation determination unit of the urban flood resilience simulation model, perform simulation calculation on the rainfall process information and the preprocessed geographical data based on the stormwater management model, and determine the temporal inundation depth data of the city under the rainfall process information, including: Divide the pipe network water drop points of the urban pipe network data in the preprocessed geographical data into corresponding sub-catchments through a preset algorithm; Calculate the average slope and impervious area ratio of the sub-catchments through ArcGIS spatial analysis; Perform coupled simulation of a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model through the stormwater management model, and set the simulation parameters; Based on the rainfall process information, the preprocessed geographical data, the average slope and impervious area ratio of the sub-catchments, and taking the sub-catchments as the simulation unit, perform simulation calculation through the time synchronization and water volume exchange mechanism of the coupled one-dimensional hydrodynamic model and two-dimensional hydrodynamic model, and determine the temporal inundation 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, characterized in that The flood resilience determination unit of the urban flood resilience simulation model determines the flood resilience of the city based on the time-series inundation depth data of the city, including: The flood resilience determination unit of the urban flood resilience simulation model determines the corresponding time-series traffic reliability data based on the time-series inundation depth data of the city; By substituting all independent paths and time-series traffic reliability data in the city into the urban system performance quantification algorithm for calculation, the urban system performance curve is determined. The expression of the urban system performance quantification algorithm is , where represents the quantified index value of the urban system performance 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; represents the independent path the traffic reliability value at time t; is the number of all independent paths between the i-th node and the j-th node; among them, the traffic reliability value at time t is the composition of the independent path the traffic reliability of each section of the road product; The ratio of the integral of the urban system performance curve on the time axis during the period from the start time of the flood disaster event to the time when the urban system returns 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.

8. An optimization method for an adaptation strategy based on urban flood resilience according to claim 7, characterized in that, Determine the independent paths in the urban road network, including: Determine the bifurcation positions of the bifurcated roads in the urban roads as road nodes, and construct two road nodes as a node pair; Use the target algorithm to repeatedly search for new shortest paths in the remaining paths between node pairs that do not coincide with the previously searched shortest paths; Determine all the searched shortest paths as independent paths to obtain the independent paths in the urban road network.

9. The adaptive strategy optimization method based on urban flood resilience according to claim 1, characterized in that, Based on the urban flood resilience simulation results of various types of adaptation strategies under the target rainfall scenario, determine the target type of adaptation strategy under the target rainfall scenario, 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 respectively, the value of flood resilience improvement per unit cost of each adaptive strategy type under the target rainfall scenario is determined. The expression of the cost-benefit index algorithm is , where I is the degree of flood resilience improvement per unit cost; is the flood resilience of the city after implementing the corresponding adaptive strategy type under the target rainfall scenario, is the flood resilience of the city without implementing the adaptive strategy type under the target rainfall scenario; C is the amount spent on implementing the corresponding adaptive strategy type under the target rainfall scenario; Determine the type of adaptation strategy with the largest increase in flood resilience per unit cost as the target type of adaptation strategy under the target rainfall scenario.

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