Urban flood response toughness evaluation method and system based on HVEDR model

Abstract

The invention discloses an urban flood response toughness evaluation method and system based on an HVEDR model, and the method comprises the steps: firstly constructing an HVEDR model through employing the evaluation dimensions of disaster-inducing performance, vulnerability, exposure, defensiveness and restoration, and then building a hierarchical structure model based on the HVEDR model according to flood disaster factors and the attribute relation between the flood disaster factors. Then, on the basis of the hierarchical structure model, a scale method is adopted to carry out importance comparison on the criterion layer and the index layer so as to obtain hierarchical scales; according to the method, the function of accurately evaluating the urban flood response toughness by surrounding the evaluation dimensions of disaster-causing property, vulnerability, exposure degree, defensiveness and restorability is realized, and the evaluation indexes comprise the aspects of society, economy, environment and infrastructure, so that the flood response elasticity can be objectively, accurately and comprehensively evaluated; and the method also has the effects of being scientific, rigorous, comprehensive and operable, and is suitable for wide popularization and application.

Description

Technical Field

[0001] The present invention relates to the technical field of urban flood response resilience assessment, and particularly relates to an urban flood response resilience assessment method and system based on the HVEDR model. Background Art

[0002] Urban floods are one of the most common and destructive natural disasters. In the past few decades, they have caused significant socio-economic losses in many cities around the world and posed a serious threat to human health and safety. More worryingly, urbanization and climate change are expected to exacerbate the scale and intensity of urban floods. The increasing urban flood risk has brought new challenges to urban managers in flood control and hindered the sustainable development of urban ecosystems. These negative impacts have led to the adoption of sustainable rainwater management strategies globally. Among them, the concept of resilience provides a new perspective and innovative approach for managing urban flood disasters. Resilience provides a practical framework for preventing and mitigating the impacts of various disasters faced by modern cities. When the concept of resilience is applied to the field of urban flood disasters, the concept of urban flood response resilience emerges accordingly.

[0003] Currently, the existing Pressure-State-Response framework generally does not consider the impact of the recovery ability of urban systems on urban flood response resilience, and only calculates urban flood response resilience through the mathematical combination of sub-dimensions such as society, economy, and infrastructure. This makes it impossible to consider the disaster-causing factors, vulnerability factors, protection measures, and post-disaster recovery in the process of urban flood response resilience assessment, and at the same time, it cannot cover social, economic, infrastructure, community, and environmental factors. This not only makes the flood response resilience assessment results prone to large deviations but also seriously affects the implementation of urban flood response strategies; therefore, it is necessary to design an urban flood response resilience assessment method and system based on the HVEDR model. Summary of the Invention

[0004] The object of the present invention is to overcome the deficiencies of the prior art and, in order to better and effectively solve the problem that the existing Pressure-State-Response framework generally does not consider the impact of the recovery ability of the urban system on the resilience of urban flood response, and only calculates the resilience of urban flood response through the mathematical combination of sub-dimensions such as society, economy, and infrastructure. This makes it impossible to consider the disaster-causing factors, vulnerability factors, protection measures, and post-disaster recovery in the evaluation process of urban flood response resilience, and at the same time, it cannot cover social, economic, infrastructure, community, and environmental factors. This not only makes the evaluation results of flood response resilience prone to large deviations but also seriously affects the implementation of urban flood response strategies. The present invention provides a method and system for evaluating the resilience of urban flood response based on the HVEDR model, which realizes the function of accurately evaluating the resilience of urban flood response around the evaluation dimensions of disaster-causing property, vulnerability, exposure, defensiveness, and restorability. Moreover, the evaluation indicators cover all aspects of society, economy, environment, and infrastructure, which can not only objectively, accurately, and comprehensively evaluate the flood response elasticity but also have the effects of scientific rigor, comprehensiveness, and operability. At the same time, it comprehensively considers the disaster-causing factors, vulnerability factors, protection measures, and post-disaster recovery.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows: A method for evaluating the resilience of urban flood response based on the HVEDR model, comprising the following steps: Step A: Construct an HVEDR model by using the evaluation dimensions of disaster-causing property, vulnerability, exposure, defensiveness, and restorability; Step B: Based on the HVEDR model and according to the impact flood disaster factors and their mutual attribute relationships, establish a hierarchical structure model; Step C: Based on the hierarchical structure model and using the scale method, respectively compare the importance of the criterion layer and the index layer to obtain the hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale; Step D: Calculate the element weights of the element importance judgment matrix, and then conduct a consistency test on the element weights; Step E: Based on the HVEDR model and using the entropy weight method, construct an evaluation index judgment matrix, and then calculate the evaluation index entropy weight by using the evaluation index judgment matrix; Step F: Use the game theory combined weight method to determine the weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weight; Step G: Establish a TOPSIS evaluation model according to the game theory comprehensive weight and calculate the flood disaster response resilience index value; Step H: Use the flood disaster response resilience index value to obtain the flood disaster response resilience level, thereby completing the evaluation operation of the resilience of urban flood response.

[0006] The above-mentioned urban flood response resilience assessment method based on the HVEDR model, step A: construct the HVEDR model using the assessment dimensions of hazard, vulnerability, exposure, defensiveness, and recoverability. Among them, the hazard specifically refers to urban flood disasters caused by precipitation driving, total rainfall, precipitation concentration, and precipitation coverage. The vulnerability specifically refers to the damage and adverse effects suffered by systems, regions, and societies when exposed to flood disasters. The exposure specifically refers to the quantity and value of the population, property, economy, farmland, and infrastructure exposed to natural disasters. The defensiveness specifically refers to the implementation of engineering and non-engineering measures during the pre-disaster and in-disaster stages of flood disasters to reduce the likelihood of disasters and mitigate the impact on human life, property, society, and the environment. The recoverability specifically refers to the ability of the region to quickly resume normal operations after a flood disaster.

[0007] The above-mentioned urban flood response resilience assessment method based on the HVEDR model, step B: establish a hierarchical structure model based on the HVEDR model and according to the flood disaster impact factors and their mutual attribute relationships. Among them, the hierarchical structure model includes an objective layer, a criterion layer, and an index layer. The objective layer is the flood disaster response resilience index. The criterion layer is hazard, vulnerability, exposure, defensiveness, and recoverability. The index layer is specific assessment indicators.

[0008] The above-mentioned urban flood response resilience assessment method based on the HVEDR model, step C: based on the hierarchical structure model, use the scaling method to compare the importance of the criterion layer and the index layer respectively to obtain the hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale. Among them, the element importance judgment matrix is used to compare the importance of each element in the index layer with each criterion in the criterion layer pairwise. The specific element importance judgment matrix is shown in formula (1). (1) Among them, is the judgment value obtained by comparison, is the reciprocal of.

[0009] The above-mentioned urban flood response resilience assessment method based on the HVEDR model, step D: calculate the element weights of the element importance judgment matrix, and then conduct a consistency test on the element weights. The specific steps are as follows. Step D1: Calculate the element weights of the element importance judgment matrix. Among them, the element weights specifically refer to calculating the maximum eigenvector of the element importance judgment matrix. The specific steps are as follows. Step D11: Normalize the elements in the same column of the element importance judgment matrix to obtain the normalized matrix , as shown in formula (2). (2) Among them, is the value of the i-th row and j-th column in the normalization matrix S, and n is the matrix dimension; Step D12, sum the elements in the same row of the normalization matrix to obtain a vector , as shown in formula (3), (3) Among them, is the sum of all the values in the i-th row of the normalization matrix S; Step D13, perform normalization processing on the vector C and obtain a vector , as shown in formula (4), (4) Among them, is the value of the i-th row of the vector , and the vector is the maximum eigenvector of the element importance judgment matrix; Step D2, perform a consistency test on the element weights, where the consistency test specifically uses the consistency index, random consistency index, and consistency ratio for the consistency test. The specific steps are as follows Step D21, calculate the consistency index CI of the maximum eigenvalue of the element importance judgment matrix, as shown in formula (5), (5); Step D22, determine the average random consistency index RI according to the matrix dimension size; Step D23, calculate the consistency ratio CR, as shown in formula (6), (6) Among them, if the consistency ratio CR < 0.1, the consistency of the element importance judgment matrix meets the requirements; otherwise, correct the element importance judgment matrix.

[0010] For the aforementioned urban flood response resilience evaluation method based on the HVEDR model, step E, construct an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method, and then calculate the entropy weight of the evaluation index using the evaluation index judgment matrix. The specific steps are as follows Step E1, construct an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method. Specifically, let be the value of the j-th evaluation index of the i-th evaluation object, then the evaluation index judgment matrix is , where m is the number of evaluation objects; Step E2. Calculate the entropy weight of evaluation indicators using the evaluation indicator judgment matrix. The specific steps are as follows: Step E21. Calculate the entropy value of the evaluation indicator, as shown in formula (7): , (7) where is the entropy value of the evaluation indicator, is the proportion of the i-th evaluation object under the j-th evaluation indicator in this indicator; Step E22. Calculate the information entropy difference coefficient, as shown in formula (8): (8) where is the information entropy difference coefficient; Step E23. Calculate the entropy weight of the evaluation indicator, as shown in formula (9): (9) where is the entropy weight of the evaluation indicator.

[0011] For the aforementioned urban flood response resilience evaluation method based on the HVEDR model, in step F, use the game theory combined weight method to determine the weight between the element weight and the entropy weight of the evaluation indicator and obtain the game theory comprehensive weight. The game theory combined weight method specifically performs a linear fitting on the weights obtained by different methods. The specific steps are as follows: Step F1. Obtain the weight vector set using different weighting methods ={ , ,…, } and calculate the linear combination of the weight vectors, as shown in formula (10): (10) where is the matrix composed of the weight vectors obtained by different weighting methods, is the optimized linear combination coefficient, is the weight vector set obtained by different weighting methods, is transpose vector of; Step F2. Use game theory to introduce different weight vectors into the protocol and compromise and calculate by optimizing the linear combination coefficient and the minimum deviation target , and then optimize the minimum deviation target to the first-order derivative condition using the matrix differential property, as shown in formula (11): (11) Among them, is the eigenvector when the deviation minimum target condition is satisfied, is the transposed vector of ; Step F3, normalize the linear combination coefficient as shown in formula (12), (12) Among them, is the linear combination coefficient of the k-th weighting method after normalization; Step F4, calculate the combined weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weight, as shown in formula (13), (13) Among them, is the game theory comprehensive weight.

[0012] The aforementioned method for evaluating the resilience of urban flood response based on the HVEDR model, step G, establish a TOPSIS evaluation model according to the game theory comprehensive weight and calculate the flood disaster response resilience index value, where the TOPSIS evaluation model specifically uses the proximity distance between the evaluation object and the target to obtain the ranking to determine the pros and cons of the evaluation object. The specific steps are as follows: Step G1, determine the evaluation criteria and evaluation weights, specifically determine the evaluation criteria of the evaluation plan and assign evaluation weights to each evaluation criterion; Step G2, standardize the judgment matrix, specifically use the original matrix to obtain a dimensionless decision matrix , as shown in formula (14), (14) Among them, is the value of the i-th row and j-th column in the dimensionless decision matrix Z; Step G3, construct a weighted decision matrix V, as shown in formula (15), (15) Among them, is the value of the i-th row and j-th column in the weighted decision matrix V; Step G4, determine the ideal solution and the negative ideal solution, specifically determine the positive ideal solution and the negative ideal solution , as shown in formula (16), (16) Step G5, calculate the similarity. Specifically, calculate the distance between each solution and the positive ideal solution and the negative ideal solution to obtain the similarity between the solution and the positive ideal solution and the negative ideal solution as shown in formula (17), (17) wherein, is the similarity between the solution and the positive ideal solution ; is the similarity between the solution and the negative ideal solution ; Step G6, calculate the flood disaster response resilience index value. Specifically, calculate the flood disaster response resilience index value according to the similarity between the solution and the positive ideal solution and the negative ideal solution as shown in formula (18), (18) wherein, is the flood disaster response resilience index value, and the closer the flood disaster response resilience index value is to 1, the better the evaluation result, and vice versa

[0013] For the aforementioned urban flood disaster response resilience evaluation method based on the HVEDR model, in step H, use the flood disaster response resilience index value to obtain the flood disaster response resilience level, thereby completing the urban flood disaster response resilience evaluation operation. Specifically, use the K-means algorithm in cluster analysis to divide the flood disaster response resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience flood disaster response resilience levels

[0014] An urban flood disaster response resilience evaluation system based on the HVEDR model, including an HVEDR model construction module, a hierarchical structure model establishment module, an importance comparison module, a weight calculation module, an entropy weight calculation module, a game theory combination module, a flood disaster response resilience index value calculation module, and a flood disaster response resilience level division module. The HVEDR model construction module is used to construct an HVEDR model by using the disaster-causing, vulnerability, exposure, defensibility, and recoverability evaluation dimensions The hierarchical structure model establishment module is used to establish a hierarchical structure model based on the HVEDR model and according to the flood disaster influencing factors and their mutual attribute relationships The importance comparison module is used to respectively compare the importance of the criterion layer and the index layer based on the hierarchical structure model by using the scale method to obtain the hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale The weight calculation module is used to calculate the element weights of the element importance judgment matrix and then conduct a consistency test on the element weights The entropy weight calculation module is used to construct a judgment matrix for evaluation indicators based on the HVEDR model and using the entropy weight method, and then calculate the entropy weight of the evaluation indicators using the judgment matrix of the evaluation indicators; The game theory combination module is used to determine the weight between the element weight and the entropy weight of the evaluation indicator using the game theory combination weight method and obtain the game theory comprehensive weight; The flood disaster response resilience index value calculation module is used to establish a TOPSIS evaluation model based on the game theory comprehensive weight and calculate the flood disaster response resilience index value; The flood disaster response resilience level division module is used to obtain the flood disaster response resilience level using the flood disaster response resilience index value, thereby completing the evaluation operation of the urban flood response resilience.

[0015] The beneficial effects of the present invention are as follows: A method and system for evaluating urban flood response resilience based on the HVEDR model of the present invention first constructs the HVEDR model using the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability, and then based on the HVEDR model and according to the flood disaster influencing factors and their mutual attribute relationships, establishes a hierarchical structure model. Then, based on the hierarchical structure model and using the scale method, the importance of the criterion layer and the index layer is compared respectively to obtain the hierarchical scale, and then according to the hierarchical scale, an element importance judgment matrix is constructed. Subsequently, the element weight of the element importance judgment matrix is calculated, and then the consistency of the element weight is tested. Then, based on the HVEDR model and using the entropy weight method, a judgment matrix for evaluation indicators is constructed, and then the entropy weight of the evaluation indicators is calculated using the judgment matrix of the evaluation indicators. Immediately afterwards, the game theory combination weight method is used to determine the weight between the element weight and the entropy weight of the evaluation indicator and obtain the game theory comprehensive weight. Then, a TOPSIS evaluation model is established based on the game theory comprehensive weight and the flood disaster response resilience index value is calculated. Finally, the flood disaster response resilience level is obtained using the flood disaster response resilience index value, thereby completing the evaluation operation of the urban flood response resilience; effectively realizing the function that the method and system for evaluating urban flood response resilience can accurately evaluate the urban flood response resilience around the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability, and the evaluation indicators cover all aspects of society, economy, environment, and infrastructure. It can not only objectively, accurately, and comprehensively evaluate the flood response elasticity, but also has the effects of scientific rigor, comprehensiveness, and operability. At the same time, it comprehensively considers the disaster-causing factors, vulnerability factors, protection measures, and post-disaster recovery. By using the game theory combination weight method to determine the weight between the element weight and the entropy weight of the evaluation indicator and obtain the game theory comprehensive weight, the objectivity and reliability of the evaluation results of the urban flood response resilience are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the overall flowchart of a method for evaluating urban flood response resilience based on the HVEDR model of the present invention; Figure 2 is a schematic diagram of the working principle of the hierarchical structure model of the present invention; Figure 3 is a schematic diagram of the working principle of the game theory combined weight method of the present invention. Specific embodiments

[0017] The present invention will be further described below in conjunction with the accompanying drawings of the specification.

[0018] As Figure 1 shown, a method for evaluating the resilience of urban flood control based on the HVEDR model of the present invention includes the following steps: Step A: Construct an HVEDR model using the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability. Among them, the hazard is specifically the urban flood disaster caused by precipitation drive, total rainfall, precipitation concentration, and precipitation coverage factors. The vulnerability specifically refers to the damage and adverse effects suffered by systems, regions, and societies when exposed to flood disasters. The exposure is specifically the quantity and value of the population, property, economy, farmland, and infrastructure exposed to natural disasters. The defensibility is specifically the implementation of engineering and non-engineering measures during the pre-disaster and in-disaster stages of flood disasters to reduce the likelihood of disasters and mitigate the impact on human life, property, society, and the environment. The recoverability is specifically the ability of the region to quickly resume normal operation after a flood disaster.

[0019] As Figure 2 shown, Step B: Based on the HVEDR model and according to the influencing flood disaster factors and their mutual attribute relationships, establish a hierarchical structure model. Among them, the hierarchical structure model includes an objective layer, a criterion layer, and an index layer. The objective layer is the flood disaster response resilience index. The criterion layer is hazard, vulnerability, exposure, defensibility, and recoverability. The index layer is specific evaluation indicators.

[0020] Step C: Based on the hierarchical structure model and using the scaling method, compare the importance of the criterion layer and the index layer respectively to obtain a hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale. Among them, the element importance judgment matrix is used to compare the importance of each element in the index layer with each criterion in the criterion layer pairwise. The specific element importance judgment matrix is shown in formula (1). (1) Among them, is the judgment value obtained by comparison, is the reciprocal of.

[0021] Step D: Calculate the element weights of the element importance judgment matrix, and then conduct a consistency test on the element weights. The specific steps are as follows: Step D1: Calculate the element weights of the element importance judgment matrix. Specifically, calculate the maximum eigenvector of the element importance judgment matrix. The specific steps are as follows: Step D11: Normalize the elements in the same column of the element importance judgment matrix to obtain the normalized matrix , as shown in formula (2): (2) where is the value of the i-th row and j-th column in the normalized matrix S, and n is the matrix dimension; Step D12: Sum the elements in the same row of the normalized matrix to obtain the vector , as shown in formula (3): (3) where is the sum of all values in the i-th row of the normalized matrix S; Step D13: Normalize the vector C to obtain the vector , as shown in formula (4): (4) where is the value of the i-th row of the vector , and the vector is the maximum eigenvector of the element importance judgment matrix; Step D2: Conduct a consistency test on the element weights. Specifically, use the consistency index, random consistency index, and consistency ratio to conduct the consistency test. The specific steps are as follows: Step D21: Calculate the consistency index CI of the maximum eigenvalue of the element importance judgment matrix, as shown in formula (5): (5); Step D22: Determine the average random consistency index RI according to the matrix dimension size; Step D23: Calculate the consistency ratio CR, as shown in formula (6): (6) where, if the consistency ratio CR < 0.1, the consistency of the element importance judgment matrix meets the requirements; otherwise, correct the element importance judgment matrix.

[0022] Step E: Based on the HVEDR model, construct an evaluation index judgment matrix using the entropy weight method, and then calculate the evaluation index entropy weight using the evaluation index judgment matrix. The specific steps are as follows: Step E1, construct an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method. Specifically, let be the value of the j-th evaluation index of the i-th evaluation object. Then the evaluation index judgment matrix is , where m is the number of evaluation objects; Step E2, calculate the entropy weight of the evaluation index using the evaluation index judgment matrix. The specific steps are as follows. Step E21, calculate the entropy value of the evaluation index, as shown in formula (7). , (7) where, is the entropy value of the evaluation index, is the proportion of the i-th evaluation object under the j-th evaluation index in this index; Step E22, calculate the information entropy difference coefficient, as shown in formula (8). (8) where, is the information entropy difference coefficient; Step E23, calculate the entropy weight of the evaluation index, as shown in formula (9). (9) where, is the entropy weight of the evaluation index.

[0023] As Figure 3 shown, step F, use the game theory combination weight method to determine the weight between the element weight and the entropy weight of the evaluation index and obtain the game theory comprehensive weight. The game theory combination weight method specifically linearly fits the weights obtained by different methods. The specific steps are as follows. Step F1, obtain the weight vector set = { , , …, } using different weighting methods and calculate the linear combination of the weight vectors, as shown in formula (10). (10) where, is the matrix composed of the weight vectors obtained by different weighting methods, is the optimized linear combination coefficient, is the weight vector set obtained by different weighting methods, is 's transposed vector; Step F2, use game theory to introduce different weight vectors into the protocol and compromise and calculate through the optimized linear combination coefficient and Minimum deviation objective Then, using the matrix differential property, the minimum deviation objective is optimized into the first derivative condition, as shown in Equation (11): (11) where is the eigenvector when the minimum deviation objective condition is satisfied, and is the transpose vector of Step F3: Normalize the linear combination coefficient as shown in Equation (12): (12) where is the linear combination coefficient of the k-th weighted method after normalization; Step F4: Calculate the combined weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weight, as shown in Equation (13): (13) where is the game theory comprehensive weight.

[0024] Step G: Establish a TOPSIS evaluation model based on the game theory comprehensive weight and calculate the flood disaster response resilience index value. The TOPSIS evaluation model specifically uses the proximity distance between the evaluation object and the target to obtain the ranking to determine the advantages and disadvantages of the evaluation object. The specific steps are as follows: Step G1: Determine the evaluation criteria and evaluation weights, specifically determine the evaluation criteria of the evaluation scheme and assign evaluation weights to each evaluation criterion; Step G2: Standardize the judgment matrix, specifically use the original matrix to obtain the dimensionless decision matrix as shown in Equation (14): (14) where is the value of the i-th row and j-th column in the dimensionless decision matrix Z; Step G3: Construct the weighted decision matrix V, as shown in Equation (15): (15) where is the value of the i-th row and j-th column in the weighted decision matrix V; Step G4: Determine the ideal solution and the negative ideal solution, specifically determine the positive ideal solution and the negative ideal solution according to the maximum and minimum values of each evaluation criterion, as shown in Equation (16):​ (16) Step G5, calculate the similarity. Specifically, calculate the distances between each solution and the positive ideal solution and the negative ideal solution to obtain the similarity between the solution and the positive ideal solution and the negative ideal solution as shown in formula (17). (17) where is the similarity between the solution and the positive ideal solution and is the similarity between the solution and the negative ideal solution ; Step G6, calculate the flood disaster response resilience index value. Specifically, calculate the flood disaster response resilience index value according to the similarities between the solution and the positive ideal solution and the negative ideal solution as shown in formula (18). (18) where is the flood disaster response resilience index value. The closer the flood disaster response resilience index value is to 1, the better the evaluation result; otherwise, the worse the result.

[0025] Step H, obtain the flood disaster response resilience level using the flood disaster response resilience index value, thereby completing the urban flood disaster response resilience evaluation operation. Specifically, use the K-means algorithm in cluster analysis to divide the flood disaster response resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience flood disaster response resilience levels.

[0026] An urban flood response resilience evaluation system based on the HVEDR model, comprising an HVEDR model construction module, a hierarchical structure model establishment module, an importance comparison module, a weight calculation module, an entropy weight calculation module, a game theory combination module, a flood disaster response resilience index value calculation module, and a flood disaster response resilience level division module. The HVEDR model construction module is used to construct the HVEDR model by using the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability. The hierarchical structure model establishment module is used to establish a hierarchical structure model based on the HVEDR model and according to the flood disaster impact factors and their mutual attribute relationships. The importance comparison module is used to respectively compare the importance of the criterion layer and the index layer based on the hierarchical structure model by using the scale method to obtain the hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale. The weight calculation module is used to calculate the element weights of the element importance judgment matrix and then conduct a consistency test on the element weights. The entropy weight calculation module is used to construct an evaluation index judgment matrix based on the HVEDR model by using the entropy weight method, and then calculate the evaluation index entropy weight by using the evaluation index judgment matrix. The game theory combination module is used to determine the weight between the element weight and the evaluation index entropy weight by using the game theory combination weight method and obtain the game theory comprehensive weight. The flood disaster response resilience index value calculation module is used to establish a TOPSIS evaluation model according to the game theory comprehensive weight and calculate the flood disaster response resilience index value. The flood disaster response resilience level division module is used to obtain the flood disaster response resilience level by using the flood disaster response resilience index value, thereby completing the urban flood response resilience evaluation operation.

[0027] In summary, for a method and system for evaluating the resilience of urban flood response based on the HVEDR model of the present invention, first, an HVEDR model is constructed using the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability. Then, based on the HVEDR model and according to the flood disaster impact factors and their mutual attribute relationships, a hierarchical structure model is established. Next, based on the hierarchical structure model and using the scaling method, the importance of the criterion layer and the index layer is compared respectively to obtain the hierarchical scale. Then, an element importance judgment matrix is constructed according to the hierarchical scale. Subsequently, the element weights of the element importance judgment matrix are calculated, and then the consistency of the element weights is tested. Then, based on the HVEDR model and using the entropy weight method, an evaluation index judgment matrix is constructed. Next, the entropy weight of the evaluation index is calculated using the evaluation index judgment matrix. Immediately afterwards, the game theory combined weight method is used to determine the weights between the element weights and the entropy weight of the evaluation index and obtain the game theory comprehensive weight. Then, a TOPSIS evaluation model is established according to the game theory comprehensive weight and the flood disaster response resilience index value is calculated. Finally, the flood disaster response resilience level is obtained using the flood disaster response resilience index value to complete the evaluation operation of the urban flood response resilience; effectively realizing that the method and system for evaluating the resilience of urban flood response have the function of accurately evaluating the resilience of urban flood response around the evaluation dimensions of hazard, vulnerability, exposure, defensibility, and recoverability, and the evaluation indexes cover all aspects of society, economy, environment, and infrastructure. It can not only objectively, accurately, and comprehensively evaluate the flood response elasticity, but also has the effects of scientific rigor, comprehensiveness, and operability. At the same time, it comprehensively considers the disaster-causing factors, vulnerability factors, protection measures, and post-disaster recovery. By using the game theory combined weight method to determine the weights between the element weights and the entropy weight of the evaluation index and obtain the game theory comprehensive weight, the objectivity and reliability of the evaluation results of the urban flood response resilience are guaranteed.

[0028] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the resilience of urban flood response based on the HVEDR model, characterized in that: including the following steps, Step A, constructing the HVEDR model using the evaluation dimensions of disaster-causing, vulnerability, exposure, defensiveness, and recoverability; Step B, establishing a hierarchical structure model based on the HVEDR model and according to the flood disaster influencing factors and their mutual attribute relationships; Step C, based on the hierarchical structure model and using the scaling method to respectively compare the importance of the criterion layer and the index layer to obtain the hierarchical scale, and then constructing an element importance judgment matrix according to the hierarchical scale; Step D, calculating the element weights of the element importance judgment matrix, and then conducting a consistency test on the element weights; Step E, based on the HVEDR model and using the entropy weight method to construct an evaluation index judgment matrix, and then calculating the evaluation index entropy weight using the evaluation index judgment matrix; Step F, using the game theory combination weight method to determine the weights between the element weights and the evaluation index entropy weight and obtaining the game theory comprehensive weight; Step G, establishing a TOPSIS evaluation model according to the game theory comprehensive weight and calculating the flood disaster response resilience index value; Step H, obtaining the flood disaster response resilience level using the flood disaster response resilience index value, thereby completing the urban flood response resilience evaluation operation.

2. The urban flood response resilience assessment method based on the HVEDR model according to claim 1, characterized in that: Step A, constructing the HVEDR model using the evaluation dimensions of disaster-causing, vulnerability, exposure, defensiveness, and recoverability, wherein the disaster-causing is specifically the urban flood disaster caused by precipitation drive, total rainfall, precipitation concentration, and precipitation coverage factors, the vulnerability is specifically the damage and adverse effects suffered by the system, region, and society when exposed to flood disasters, the exposure is specifically the quantity and value of the population, property, economy, farmland, and infrastructure exposed to natural disasters, the defensiveness is specifically the implementation of engineering and non-engineering measures during the pre-flood disaster and in-flood disaster stages to reduce the possibility of the disaster occurring and mitigate the impact on human life, property, society, and the environment, and the recoverability is specifically the ability of the region to quickly resume normal operation after the flood disaster.

3. The urban flood response resilience assessment method based on the HVEDR model according to claim 2, wherein: Step B, establishing a hierarchical structure model based on the HVEDR model and according to the flood disaster influencing factors and their mutual attribute relationships, wherein the hierarchical structure model includes an objective layer, a criterion layer, and an index layer, the objective layer is the flood disaster response resilience index, the criterion layer is disaster-causing, vulnerability, exposure, defensiveness, and recoverability, and the index layer is specific evaluation indicators.

4. The urban flood response resilience assessment method based on the HVEDR model according to claim 3, characterized in that: Step C, based on the hierarchical structure model and using the scaling method to respectively compare the importance of the criterion layer and the index layer to obtain the hierarchical scale, and then constructing an element importance judgment matrix according to the hierarchical scale, wherein the element importance judgment matrix is used to conduct pairwise comparisons of the importance between each element in the index layer and each criterion in the criterion layer, and the specific element importance judgment matrix is shown in formula (1); （1） Among them, is the judgment value obtained by comparison, is the reciprocal of.

5. The urban flood response resilience assessment method based on the HVEDR model according to claim 4, wherein: Step D, calculating the element weights of the element importance judgment matrix, and then conducting a consistency test on the element weights. The specific steps are as follows: Step D1, calculating the element weights of the element importance judgment matrix, wherein the element weights are specifically calculating the maximum eigenvector of the element importance judgment matrix. The specific steps are as follows: Step D11, normalize the elements in the same column of the element importance judgment matrix to obtain a normalized matrix , as shown in formula (2). （2） Among them, is the value at the i-th row and j-th column of the normalization matrix S, and n is the matrix dimension; Step D12, sum the elements in the same row of the normalization matrix to obtain a vector , as shown in formula (3). （3） Among them, is the sum of all the values in the i-th row of the normalization matrix S; Step D13, normalize vector C and obtain vector , as shown in formula (4). （4） Among them, is a vector The value of the i-th row, and the vector is the maximum eigenvector of the element importance judgment matrix; Step D2, conduct a consistency test on the element weights. Specifically, the consistency test uses the consistency index, random consistency index, and consistency ratio for the consistency test. The specific steps are as follows: Step D21, calculate the maximum eigenvalue of the element importance judgment matrix of the consistency index CI, as shown in formula (5), （5）； Step D22, determine the average random consistency index RI according to the matrix dimension size; Step D23, calculate the consistency ratio CR, as shown in formula (6); （6） Among them, if the consistency ratio CR < 0.1, the consistency of the element importance judgment matrix meets the requirements. Otherwise, correct the element importance judgment matrix.

6. The urban flood response resilience assessment method based on the HVEDR model according to claim 5, characterized in that: Step E, based on the HVEDR model and using the entropy weight method, construct an evaluation index judgment matrix, and then calculate the evaluation index entropy weight using the evaluation index judgment matrix. The specific steps are as follows: Step E1: Construct an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method. Specifically, assume that is the value of the j-th evaluation index of the i-th evaluation object. Then the evaluation index judgment matrix is , where m is the number of evaluation objects; Step E2, calculate the evaluation index entropy weight using the evaluation index judgment matrix. The specific steps are as follows: Step E21, calculate the entropy value of the evaluation index, as shown in formula (7); ， （7） Among them, is the entropy value of the evaluation index, is the proportion of the i-th evaluation object under the j-th evaluation index in this index; Step E22, calculate the information entropy difference coefficient, as shown in formula (8); （8） Among them, is the information entropy difference coefficient; Step E23, calculate the evaluation index entropy weight, as shown in formula (9); （9） Among them, is the entropy weight of the evaluation index.

7. A method for evaluating the resilience of urban flood response based on the HVEDR model according to claim 6, characterized in that: Step F, use the game theory combination weight method to determine the weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weight. Specifically, the game theory combination weight method linearly fits the weights obtained by different methods. The specific steps are as follows: Step F1, obtain a set of weight vectors by using different weighting methods = { , ,…, } and calculate the linear combination of the weight vectors, as shown in formula (10). （10） Among them, is a matrix composed of weight vectors obtained by different weighting methods, is the optimized linear combination coefficient, is the set of weight vectors obtained by different weighting methods, is the transposed vector of Step F2, introduce different weight vectors into the protocol and compromise using game theory and optimize the linear combination coefficients Calculate with the minimum deviation target , and then use the matrix differential property to optimize the minimum deviation target into the first-order derivative condition, as shown in formula (11). （11） Among them, is the eigenvector when the minimum deviation target condition is satisfied, is the transposed vector of Step F3, normalize the linear combination coefficient as shown in formula (12). （12） Among them, is the linear combination coefficient of the k-th weighted method after normalization; Step F4, calculate the combined weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weight, as shown in formula (13); （13） Among them, is the comprehensive weighting of game theory.

8. A method for evaluating the resilience of urban flood response based on the HVEDR model according to claim 7, characterized in that: Step G, establish a TOPSIS evaluation model based on the game theory comprehensive weight and calculate the flood disaster response resilience index value. Specifically, the TOPSIS evaluation model uses the proximity distance between the evaluation object and the target to obtain the ranking to determine the pros and cons of the evaluation object. The specific steps are as follows: Step G1, determine the evaluation criteria and evaluation weights, specifically determine the evaluation criteria of the evaluation plan and assign evaluation weights to each evaluation criterion; Step G2, standardize the judgment matrix, specifically, obtain the dimensionless decision matrix using the original matrix , as shown in formula (14). （14） Among them, is the value of the i-th row and j-th column in the dimensionless decision matrix Z; Step G3, construct a weighted decision matrix V, as shown in formula (15); （15） Among them, is the value of the i-th row and j-th column in the weighted decision matrix V; Step G4, determine the ideal solution and the negative ideal solution, specifically determine the positive ideal solution according to the maximum and minimum values of each evaluation criterion and the negative ideal solution , as shown in formula (16). （16） Step G5, calculate the similarity, specifically by calculating the distances between each solution and the positive ideal solution and the negative ideal solution to obtain the similarity between the solution and the positive ideal solution and the negative ideal solution as shown in formula (17). （17） Among them, is the similarity between the solution and the positive ideal solution , is the similarity between the solution and the negative ideal solution ; Step G6, calculate the resilience index value for flood disaster response, specifically based on the similarity between the plan and the positive ideal solution and the negative ideal solution to calculate the resilience index value for flood disaster response, as shown in formula (18). （18） Among them, is the resilience index value for flood disaster response, where the resilience index value for flood disaster response is closer to 1, the better the evaluation result, and vice versa.

9. The urban flood response resilience assessment method based on the HVEDR model according to claim 8, characterized in that: Step H, obtain the flood disaster response resilience level using the flood disaster response resilience index value, thereby completing the urban flood response resilience evaluation operation. Specifically, use the K-means algorithm in cluster analysis to divide the flood disaster response resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience flood disaster response resilience levels.

10. A urban flood response resilience evaluation system based on the HVEDR model, wherein the specific evaluation process of the urban flood response resilience evaluation system is based on the urban flood response resilience evaluation method according to any one of claims 1-9, and is characterized in that: It includes an HVEDR model construction module, a hierarchical structure model establishment module, an importance comparison module, a weight calculation module, an entropy weight calculation module, a game theory combination module, a flood disaster response resilience index value calculation module, and a flood disaster response resilience level division module. The HVEDR model construction module is used to construct the HVEDR model using the disaster-causing, vulnerability, exposure, defensiveness, and recovery evaluation dimensions; The hierarchical structure model establishment module is used to establish a hierarchical structure model based on the HVEDR model and according to the flood disaster influencing factors and their mutual attribute relationships; The importance comparison module is used to respectively compare the importance of the criterion layer and the index layer based on the hierarchical structure model using the scale method to obtain the hierarchical scale, and then construct an element importance judgment matrix according to the hierarchical scale; The weight calculation module is used to calculate the element weights of the element importance judgment matrix, and then conduct a consistency test on the element weights; The entropy weight calculation module is used to construct an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method, and then calculate the evaluation index entropy weights using the evaluation index judgment matrix; The game theory combination module is used to determine the weights between the element weights and the evaluation index entropy weights using the game theory combination weight method and obtain the game theory comprehensive weighting; The flood disaster response resilience index value calculation module is used to establish a TOPSIS evaluation model based on the game theory comprehensive weighting and calculate the flood disaster response resilience index value; The flood disaster response resilience level division module is used to obtain the flood disaster response resilience level using the flood disaster response resilience index value, thereby completing the urban flood response resilience evaluation operation.

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