A method and system for evaluating urban flood resilience based on the HVEDR model

Abstract

The present invention discloses a method and system for evaluating urban flood response resilience based on the HVEDR model. First, the HVEDR model is constructed by adopting evaluation dimensions of hazard causality, vulnerability, exposure, defensiveness and recovery. Then, a hierarchical structure model is established based on the HVEDR model and according to factors affecting flood disasters and their mutual attribute relationships. Then, based on the hierarchical structure model, a scaling method is adopted to compare the importance of the criterion layer and the indicator layer respectively to obtain a hierarchical scale. The present invention realizes the function of accurately evaluating urban flood response resilience around the evaluation dimensions of hazard causality, vulnerability, exposure, defensiveness and recovery, and the evaluation indicators include various aspects of society, economy, environment and infrastructure. It can not only objectively, accurately and comprehensively evaluate flood response resilience, but also has the effects of scientific rigor, comprehensiveness and operability, and is suitable for wide promotion and use.

Description

Technical Field

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

[0002] Urban flooding is one of the most prevalent and destructive natural disasters. Over the past few decades, it has caused significant socioeconomic 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 increased risk of urban flooding has brought new challenges to city managers in flood prevention and hindered the sustainable development of urban ecosystems. These negative impacts have led to the adoption of sustainable stormwater management strategies worldwide. Among them, the concept of resilience provides a new perspective and innovative approach to managing urban flood disasters. Resilience provides a practical framework for preventing and mitigating the impacts of various disasters facing modern cities. When the concept of resilience is applied to the field of urban flood disasters, the concept of urban flood resilience is naturally born.

[0003] At present, the existing pressure-state-response framework generally does not consider the impact of the recovery capacity of urban systems on urban flood resilience, and only calculates urban flood resilience through a mathematical combination of sub-dimensions such as social, economic and infrastructure aspects. This makes it impossible to consider disaster-causing factors, vulnerability factors, protection measures and post-disaster recovery in the assessment process of urban flood resilience. At the same time, it also fails to cover social, economic, infrastructure, community and environmental factors. Not only does it make the flood resilience assessment results prone to large deviations, but it also seriously affects the implementation of urban flood response strategies. Therefore, it is necessary to design an urban flood resilience assessment method and system based on the HVEDR model. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and to better and effectively solve the problem that the existing pressure-state-response framework generally does not consider the impact of the resilience of the urban system on urban flood resilience. It only calculates urban flood resilience through a mathematical combination of sub-dimensions such as social, economic and infrastructure. This makes it impossible to consider disaster-causing factors, vulnerability factors, protection measures and post-disaster recovery in the evaluation process of urban flood resilience. At the same time, it also fails to cover social, economic, infrastructure, community and environmental factors. Not only does it make the flood resilience evaluation results prone to large deviations, but it also seriously affects the implementation of urban flood response strategies. The present invention provides an urban flood resilience evaluation method and system based on the HVEDR model. The method realizes the function of accurately evaluating urban flood resilience based on the evaluation dimensions of hazard causality, vulnerability, exposure, defense and recovery. The evaluation indicators include various aspects of society, economy, environment and infrastructure. It can not only objectively, accurately and comprehensively evaluate flood response resilience, but also has the effect of scientific rigor, comprehensiveness and operability, while fully considering 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:

[0006] A method for evaluating urban flood resilience based on the HVEDR model includes the following steps:

[0007] Step A: Construct the HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defense, and resilience;

[0008] Step B: Based on the HVEDR model and the factors affecting flood disasters and their mutual attribute relationships, a hierarchical structure model is established;

[0009] Step C: Based on the hierarchical structure model, the importance of the criterion layer and the indicator layer are compared by using the scaling method to obtain the hierarchical scale, and then the element importance judgment matrix is ​​constructed according to the hierarchical scale;

[0010] Step D, calculating the element weights of the element importance judgment matrix, and then performing a consistency check on the element weights;

[0011] Step E: constructing an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method, and then calculating the evaluation index entropy weight using the evaluation index judgment matrix;

[0012] Step F, using 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 weighting;

[0013] Step G: Establish a TOPSIS evaluation model based on comprehensive weighting of game theory and calculate the resilience index value of flood disaster response;

[0014] Step H: Use the flood disaster response resilience index value to obtain the flood disaster response resilience level, thereby completing the urban flood response resilience assessment task.

[0015] The aforementioned urban flood resilience assessment method based on the HVEDR model, step A, constructs the HVEDR model using the assessment dimensions of hazard susceptibility, vulnerability, exposure, defensiveness and resilience, wherein the hazard susceptibility specifically refers to urban flood disasters 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 specifically refers to the number 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 before and during the flood disaster to reduce the possibility of disasters and mitigate the impact on human life, property, society and the environment; the resilience specifically refers to the ability of a region to quickly resume normal operations after a flood disaster.

[0016] The aforementioned urban flood resilience assessment method based on the HVEDR model, step B, establishes a hierarchical model based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them, wherein the hierarchical model includes a target layer, a criterion layer and an indicator layer, the target layer is the flood disaster resilience index, the criterion layer is the hazard causality, vulnerability, exposure, defensiveness and resilience, and the indicator layer is the specific assessment indicator.

[0017] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step C is to compare the importance of the criterion layer and the indicator layer respectively based on the hierarchical structure model and adopt the scaling method to obtain the hierarchical scale, and then construct the element importance judgment matrix according to the hierarchical scale, wherein the element importance judgment matrix is ​​used to compare the importance between each element in the indicator layer and each criterion in the criterion layer. The specific element importance judgment matrix is ​​shown in formula (1).

[0018] (1)

[0019] in, is the judgment value obtained by comparison, for The reciprocal of .

[0020] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step D is to calculate the element weights of the element importance judgment matrix and then perform a consistency test on the element weights. The specific steps are as follows:

[0021] Step D1, calculate the element weight of the element importance judgment matrix, wherein the element weight is specifically the maximum eigenvector of the element importance judgment matrix. The specific steps are as follows:

[0022] Step D11, normalize the elements in the same column of the element importance judgment matrix and obtain a normalized matrix , as shown in formula (2),

[0023] (2)

[0024] in, is the value of the i-th row and j-th column in the normalized matrix S, and n is the matrix dimension;

[0025] Step D12, normalize the matrix The vector is obtained by summing the elements of the same row , as shown in formula (3),

[0026] (3)

[0027] in, is the sum of all values ​​in row i of the normalized matrix S;

[0028] Step D13, normalize the vector C and obtain the vector , as shown in formula (4),

[0029] (4)

[0030] in, is a vector The value of the i-th row, the vector is the maximum eigenvector of the element importance judgment matrix;

[0031] Step D2: Perform consistency check on the element weights, wherein the consistency check is performed using consistency index, random consistency index and consistency ratio. The specific steps are as follows:

[0032] Step D21, calculate the maximum eigenvalue of the element importance judgment matrix The consistency index CI is as shown in formula (5).

[0033] (5);

[0034] Step D22, determining the average random consistency index RI according to the matrix dimension;

[0035] Step D23, calculate the consistency ratio CR, as shown in formula (6),

[0036] (6)

[0037] Among them, if the consistency ratio CR is less than 0.1, the consistency of the element importance judgment matrix meets the requirements; otherwise, the element importance judgment matrix is ​​modified.

[0038] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step E is to construct an assessment indicator judgment matrix based on the HVEDR model and the entropy weight method, and then use the assessment indicator judgment matrix to calculate the entropy weight of the assessment indicator. The specific steps are as follows:

[0039] Step E1: Based on the HVEDR model and using the entropy weight method, the evaluation index judgment matrix is ​​constructed. is the value of the jth evaluation index of the i-th evaluation object, then the evaluation index judgment matrix is , where m is the evaluation object;

[0040] Step E2, using the evaluation index judgment matrix to calculate the evaluation index entropy weight, the specific steps are as follows:

[0041] Step E21, calculate the entropy value of the evaluation index, as shown in formula (7),

[0042] , (7)

[0043] in, is the entropy value of the evaluation index, is the proportion of the i-th evaluation object under the j-th evaluation indicator;

[0044] Step E22, calculate the information entropy difference coefficient, as shown in formula (8),

[0045] (8)

[0046] in, is the information entropy difference coefficient;

[0047] Step E23, calculate the entropy weight of the evaluation index, as shown in formula (9),

[0048] (9)

[0049] in, is the evaluation index entropy weight.

[0050] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step F uses the game theory combined weight method to determine the weights between the element weights and the evaluation index entropy weights and obtain the game theory comprehensive weighting. The game theory combined weight method specifically performs linear fitting on the weights obtained by different methods. The specific steps are as follows:

[0051] Step F1: Use different weighting methods to obtain a set of weight vectors = { , ,…, } and calculate the linear combination of weight vectors, as shown in formula (10),

[0052] (10)

[0053] in, The matrix composed of weight vectors obtained by different weighting methods, To optimize the linear combination coefficients, To obtain a set of weight vectors using different weighting methods, for The transposed vector of

[0054] Step F2, using game theory to introduce different weight vectors into the agreement and compromise and optimizing the linear combination coefficients calculate and Minimum deviation target , then the matrix differential property will minimize the deviation target The optimization is the first-order derivative condition, as shown in formula (11),

[0055] (11)

[0056] in, for and The eigenvector when the minimum deviation target condition is met, for The transposed vector of

[0057] Step F3, linear combination coefficients Normalization, as shown in formula (12),

[0058] (12)

[0059] in, is the linear combination coefficient of the kth weighting method after normalization;

[0060] Step F4, calculate the combined weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weighting, as shown in formula (13),

[0061] (13)

[0062] in, Comprehensively empowering game theory.

[0063] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step G is to establish a TOPSIS assessment model based on comprehensive weighting in game theory and calculate the flood resilience index value. The TOPSIS assessment model specifically uses the close distance between the assessment object and the target to rank and determine the quality of the assessment object. The specific steps are as follows:

[0064] Step G1, determining the evaluation criteria and evaluation weights, specifically determining the evaluation criteria of the evaluation plan and assigning evaluation weights to each evaluation criterion;

[0065] Step G2, standardize the judgment matrix, specifically use the original matrix to obtain the dimensionless decision matrix , as shown in formula (14),

[0066] (14)

[0067] in, is the value of row i and column j in the dimensionless decision matrix Z;

[0068] Step G3, construct the weighted decision matrix V, as shown in formula (15),

[0069] (15)

[0070] in, is the value of the i-th row and j-th column in the weighted decision matrix V;

[0071] Step G4: Determine the ideal solution and the negative ideal solution. Specifically, determine the positive ideal solution based on the maximum and minimum values ​​of each evaluation criterion. and negative ideal solutions , as shown in formula (16),

[0072] (16)

[0073] Step G5, calculate the similarity, specifically by calculating the distance between each solution and the positive ideal solution and the negative ideal solution to obtain the solution and the positive ideal solution. and negative ideal solutions The similarity of is shown in formula (17).

[0074] (17)

[0075] in, For the solution and the ideal solution The similarity of The solution and negative ideal solution similarity;

[0076] Step G6, calculate the flood disaster resilience index value, specifically based on the scheme and the positive ideal solution and negative ideal solutions The flood disaster resilience index value is calculated based on the similarity of , as shown in formula (18):

[0077] (18)

[0078] in, is the resilience index value for flood disaster response, where The closer it is to 1, the better the evaluation result, and vice versa.

[0079] In the aforementioned urban flood resilience assessment method based on the HVEDR model, step H uses the flood disaster resilience index value to obtain the flood disaster resilience level, thereby completing the urban flood resilience assessment task. Specifically, the K-means algorithm in cluster analysis is used to divide the flood disaster resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience and low resilience flood disaster resilience levels.

[0080] An urban flood resilience assessment system based on the HVEDR model includes an HVEDR model construction module, a hierarchical model establishment module, an importance comparison module, a weight calculation module, an entropy weight calculation module, a game theory combination module, a flood disaster resilience index value calculation module, and a flood disaster resilience grading module. The HVEDR model construction module is used to construct the HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defensiveness, and resilience.

[0081] The hierarchical structure model building module is used to build a hierarchical structure model based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them;

[0082] The importance comparison module is used to compare the importance of the criterion layer and the indicator layer based on the hierarchical structure model and adopt the scaling method to obtain the hierarchical scale, and then construct the element importance judgment matrix according to the hierarchical scale;

[0083] The weight calculation module is used to calculate the element weights of the element importance judgment matrix and then perform consistency check on the element weights;

[0084] The entropy weight calculation module is used to construct an evaluation index judgment matrix based on the HVEDR model and adopt the entropy weight method, and then use the evaluation index judgment matrix to calculate the evaluation index entropy weight;

[0085] 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 weighting;

[0086] The flood disaster response resilience index value calculation module is used to establish a TOPSIS evaluation model based on comprehensive weighting of game theory and calculate the flood disaster response resilience index value;

[0087] The flood disaster response resilience level classification 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 assessment task.

[0088] The beneficial effects of the present invention are as follows: the present invention provides an urban flood resilience assessment method and system based on the HVEDR model, firstly, the HVEDR model is constructed by adopting the assessment dimensions of hazard hazard, vulnerability, exposure, defense and recovery, then a hierarchical structure model is established based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them, then the importance of the criterion layer and the indicator layer are compared respectively based on the hierarchical structure model and the scaling method is adopted to obtain a hierarchical scale, then an element importance judgment matrix is ​​constructed according to the hierarchical scale, then the element weights of the element importance judgment matrix are calculated, then the element weights are checked for consistency, then an assessment index judgment matrix is ​​constructed based on the HVEDR model and the entropy weight method is adopted, then the entropy weights of the assessment indexes are calculated using the assessment index judgment matrix, then the weights between the element weights and the entropy weights of the assessment indexes are determined using the game theory combined weight method and the game theory comprehensive weighting is obtained, then Based on the comprehensive weighting of game theory, a TOPSIS evaluation model is established and the flood disaster response resilience index value is calculated. Finally, the flood disaster response resilience index value is used to obtain the flood disaster response resilience level, thereby completing the urban flood response resilience evaluation task; it effectively realizes that the urban flood response resilience evaluation method and system have the function of accurately evaluating urban flood response resilience around the evaluation dimensions of disaster causality, vulnerability, exposure, defense and recovery, and the evaluation indicators include various aspects of society, economy, environment and infrastructure. It can not only objectively, accurately and comprehensively evaluate flood response resilience, but also has scientific rigor, comprehensiveness and operability. At the same time, it fully considers disaster-causing factors, vulnerability factors, protection measures and post-disaster recovery. By using the game theory combined weight method to determine the weight between the element weight and the evaluation indicator entropy weight and obtain the game theory comprehensive empowerment, the objectivity and reliability of the urban flood response resilience evaluation results are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is an overall flow chart of an urban flood resilience assessment method based on the HVEDR model of the present invention;

[0090] Figure 2 It is a schematic diagram of the working principle of the hierarchical structure model of the present invention;

[0091] Figure 3 It is a schematic diagram of the working principle of the game theory combined weight method of the present invention. DETAILED DESCRIPTION

[0092] The present invention will be further described below with reference to the accompanying drawings.

[0093] like Figure 1 As shown, the present invention provides an urban flood resilience assessment method based on the HVEDR model, comprising the following steps:

[0094] Step A, constructing the HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defense and resilience, where hazard susceptibility refers to urban flood disasters caused by precipitation drive, total rainfall, precipitation concentration and precipitation coverage; vulnerability refers to the damage and adverse effects suffered by systems, regions and societies when exposed to flood disasters; exposure refers to the number and value of population, property, economy, farmland and infrastructure exposed to natural disasters; defense refers to the implementation of engineering and non-engineering measures before and during flood disasters to reduce the possibility of disasters and mitigate the impact on human life, property, society and the environment; resilience refers to the ability of a region to quickly resume normal operations after a flood disaster.

[0095] like Figure 2 As shown, step B is to establish a hierarchical model based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them, wherein the hierarchical model includes a target layer, a criterion layer and an indicator layer, the target layer is the flood disaster response resilience index, the criterion layer is the hazard causality, vulnerability, exposure, defense and recovery, and the indicator layer is the specific evaluation indicator.

[0096] Step C: Based on the hierarchical structure model, the importance of the criterion layer and the indicator layer are compared by scaling method to obtain the hierarchical scale. Then, the element importance judgment matrix is ​​constructed according to the hierarchical scale. The element importance judgment matrix is ​​used to compare the importance of each element in the indicator layer with each criterion in the criterion layer. The specific element importance judgment matrix is ​​shown in formula (1).

[0097] (1)

[0098] in, is the judgment value obtained by comparison, for The reciprocal of .

[0099] Step D: Calculate the element weights of the element importance judgment matrix and then perform consistency check on the element weights. The specific steps are as follows:

[0100] Step D1, calculate the element weight of the element importance judgment matrix, wherein the element weight is specifically the maximum eigenvector of the element importance judgment matrix. The specific steps are as follows:

[0101] Step D11, normalize the elements in the same column of the element importance judgment matrix and obtain a normalized matrix , as shown in formula (2),

[0102] (2)

[0103] in, is the value of the i-th row and j-th column in the normalized matrix S, and n is the matrix dimension;

[0104] Step D12, normalize the matrix The vector is obtained by summing the elements of the same row , as shown in formula (3),

[0105] (3)

[0106] in, is the sum of all values ​​in row i of the normalized matrix S;

[0107] Step D13, normalize the vector C and obtain the vector , as shown in formula (4),

[0108] (4)

[0109] in, is a vector The value of the i-th row, the vector is the maximum eigenvector of the element importance judgment matrix;

[0110] Step D2: Perform consistency check on the element weights, wherein the consistency check is performed using consistency index, random consistency index and consistency ratio. The specific steps are as follows:

[0111] Step D21, calculate the maximum eigenvalue of the element importance judgment matrix The consistency index CI is as shown in formula (5).

[0112] (5);

[0113] Step D22, determining the average random consistency index RI according to the matrix dimension;

[0114] Step D23, calculate the consistency ratio CR, as shown in formula (6),

[0115] (6)

[0116] Among them, if the consistency ratio CR is less than 0.1, the consistency of the element importance judgment matrix meets the requirements; otherwise, the element importance judgment matrix is ​​modified.

[0117] Step E: Based on the HVEDR model and using the entropy weight method, the evaluation index judgment matrix is ​​constructed, and then the evaluation index entropy weight is calculated using the evaluation index judgment matrix. The specific steps are as follows:

[0118] Step E1: Based on the HVEDR model and using the entropy weight method, the evaluation index judgment matrix is ​​constructed. is the value of the jth evaluation index of the i-th evaluation object, then the evaluation index judgment matrix is , where m is the evaluation object;

[0119] Step E2, using the evaluation index judgment matrix to calculate the evaluation index entropy weight, the specific steps are as follows:

[0120] Step E21, calculate the entropy value of the evaluation index, as shown in formula (7),

[0121] , (7)

[0122] in, is the entropy value of the evaluation index, is the proportion of the i-th evaluation object under the j-th evaluation indicator;

[0123] Step E22, calculate the information entropy difference coefficient, as shown in formula (8),

[0124] (8)

[0125] in, is the information entropy difference coefficient;

[0126] Step E23, calculate the entropy weight of the evaluation index, as shown in formula (9),

[0127] (9)

[0128] in, is the evaluation index entropy weight.

[0129] like Figure 3 As shown, in step F, the game theory combined weight method is used to determine the weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weighting. The game theory combined weight method specifically performs linear fitting on the weights obtained by different methods. The specific steps are as follows:

[0130] Step F1: Use different weighting methods to obtain a set of weight vectors = { , ,…, } and calculate the linear combination of weight vectors, as shown in formula (10),

[0131] (10)

[0132] in, The matrix composed of weight vectors obtained by different weighting methods, To optimize the linear combination coefficients, To obtain a set of weight vectors using different weighting methods, for The transposed vector of

[0133] Step F2, using game theory to introduce different weight vectors into the agreement and compromise and optimizing the linear combination coefficients calculate and Minimum deviation target , then the matrix differential property will minimize the deviation target The optimization is the first-order derivative condition, as shown in formula (11),

[0134] (11)

[0135] in, for and The eigenvector when the minimum deviation target condition is met, for The transposed vector of

[0136] Step F3, linear combination coefficients Normalization, as shown in formula (12),

[0137] (12)

[0138] in, is the linear combination coefficient of the kth weighting method after normalization;

[0139] Step F4, calculate the combined weight between the element weight and the evaluation index entropy weight and obtain the game theory comprehensive weighting, as shown in formula (13),

[0140] (13)

[0141] in, Comprehensively empowering game theory.

[0142] Step G: Based on the comprehensive weighting of game theory, a TOPSIS evaluation model is established and the flood disaster resilience index is calculated. The TOPSIS evaluation model uses the close distance between the evaluation object and the target to rank and determine the quality of the evaluation object. The specific steps are as follows:

[0143] 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;

[0144] Step G2, standardize the judgment matrix, specifically use the original matrix to obtain the dimensionless decision matrix , as shown in formula (14),

[0145] (14)

[0146] in, is the value of row i and column j in the dimensionless decision matrix Z;

[0147] Step G3, construct the weighted decision matrix V, as shown in formula (15),

[0148] (15)

[0149] in, is the value of the i-th row and j-th column in the weighted decision matrix V;

[0150] Step G4: Determine the ideal solution and the negative ideal solution. Specifically, determine the positive ideal solution based on the maximum and minimum values ​​of each evaluation criterion. and negative ideal solutions , as shown in formula (16),

[0151] (16)

[0152] Step G5, calculate the similarity, specifically by calculating the distance between each solution and the positive ideal solution and the negative ideal solution to obtain the solution and the positive ideal solution. and negative ideal solutions The similarity of is shown in formula (17).

[0153] (17)

[0154] in, For the solution and the ideal solution The similarity of The solution and negative ideal solution similarity;

[0155] Step G6, calculate the flood disaster resilience index value, specifically based on the scheme and the positive ideal solution and negative ideal solutions The flood disaster resilience index value is calculated based on the similarity of , as shown in formula (18):

[0156] (18)

[0157] in, is the resilience index value for flood disaster response, where The closer it is to 1, the better the evaluation result, and vice versa.

[0158] Step H: Use the flood disaster resilience index value to obtain the flood disaster resilience level, thereby completing the urban flood disaster resilience assessment task. Specifically, the K-means algorithm in cluster analysis is used to divide the flood disaster resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience and low resilience flood disaster resilience levels.

[0159] An urban flood response resilience evaluation system based on the HVEDR model includes an HVEDR model construction module, a hierarchical 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 classification module. The HVEDR model construction module is used to construct the HVEDR model by adopting the evaluation dimensions of hazard causality, vulnerability, exposure, defense and recovery; the hierarchical model establishment module is used to establish a hierarchical model based on the HVEDR model and according to the attribute relationships between the factors affecting flood disasters and their mutual attributes; the importance comparison module is used to compare the importance of the criterion layer and the indicator layer respectively based on the hierarchical model and the scaling method to obtain a hierarchical scale, and then construct an index value according to the hierarchical scale. An element importance judgment matrix is ​​constructed; the weight calculation module is used to calculate the element weights of the element importance judgment matrix, and then perform consistency check on the element weights; the entropy weight calculation module is used to construct an evaluation index judgment matrix based on the HVEDR model and adopt the entropy weight method, and then use the evaluation index judgment matrix to calculate the evaluation index entropy weight; the game theory combination module is used to 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 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 classification module is used to use the flood disaster response resilience index value to obtain the flood disaster response resilience level, thereby completing the urban flood response resilience evaluation task.

[0160] In summary, the present invention provides an urban flood resilience assessment method and system based on the HVEDR model. First, the HVEDR model is constructed by adopting the assessment dimensions of hazard hazard, vulnerability, exposure, defense and recovery. Then, a hierarchical structure model is established based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them. Then, based on the hierarchical structure model, the importance of the criterion layer and the indicator layer are compared by using the scaling method to obtain a hierarchical scale. Then, an element importance judgment matrix is ​​constructed according to the hierarchical scale. Then, the element weights of the element importance judgment matrix are calculated. Then, a consistency test is performed on the element weights. Then, an evaluation index judgment matrix is ​​constructed based on the HVEDR model and the entropy weight method is adopted. Then, the evaluation index entropy weight is calculated using the evaluation index judgment matrix. Then, the game theory combined weight method is used to determine the weights between the element weights and the evaluation index entropy weights and obtain the game theory comprehensive weighting. Game theory comprehensive weighting is used to establish a TOPSIS evaluation model and calculate the flood disaster response resilience index value. Finally, the flood disaster response resilience index value is used to obtain the flood disaster response resilience level to complete the urban flood response resilience evaluation task; it effectively realizes that the urban flood response resilience evaluation method and system have the function of accurately evaluating urban flood response resilience around the evaluation dimensions of disaster causality, vulnerability, exposure, defense and recovery, and the evaluation indicators include various aspects of society, economy, environment and infrastructure. It can not only objectively, accurately and comprehensively evaluate flood response resilience, but also has scientific rigor, comprehensiveness and operability. At the same time, it fully considers disaster-causing factors, vulnerability factors, protection measures and post-disaster recovery. By using the game theory combined weight method to determine the weight between the element weight and the evaluation indicator entropy weight and obtaining the game theory comprehensive weighting, the objectivity and reliability of the urban flood response resilience evaluation results are guaranteed.

[0161] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating urban flood resilience based on the HVEDR model, characterized by: The following steps are included: Step A: Construct the HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defense, and resilience; Step B: Based on the HVEDR model and the factors affecting flood disasters and their mutual attribute relationships, a hierarchical structure model is established; Step C: Based on the hierarchical structure model, the importance of the criterion layer and the indicator layer are compared by using the scaling method to obtain the hierarchical scale, and then the element importance judgment matrix is ​​constructed according to the hierarchical scale; Step D: Calculate the element weights of the element importance judgment matrix and then perform consistency check on the element weights. The specific steps are as follows: Step D1, calculate the element weight of the element importance judgment matrix, wherein the element weight is specifically 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 and obtain a normalized matrix S=(s ij ) n×n , as shown in formula (2), Among them, s ij 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, summing the elements in the same row of the normalized matrix S to obtain a vector C = (C1, C2, ..., C n ) T , as shown in formula (3), Among them, C i is the sum of all values ​​in row i of the normalized matrix S; Step D13, normalize the vector C and obtain the vector W=(W1, W2, ..., W n ) T , as shown in formula (4), Among them, W i is the value of the i-th row of vector W, where vector W is the maximum eigenvector of the element importance judgment matrix; Step D2: Perform consistency check on the element weights, wherein the consistency check is performed using consistency index, random consistency index and consistency ratio. The specific steps are as follows: Step D21, calculate the maximum eigenvalue λ of the element importance judgment matrix max The consistency index CI is shown in formula (5). Step D22, determining the average random consistency index RI according to the matrix dimension; Step D23, calculate the consistency ratio CR, as shown in formula (6), Among them, if the consistency ratio CR is less than 0.1, the consistency of the element importance judgment matrix meets the requirements, otherwise, the element importance judgment matrix is ​​modified; Step E: constructing an evaluation index judgment matrix based on the HVEDR model and using the entropy weight method, and then calculating the evaluation index entropy weight using the evaluation index judgment matrix; Step F, using 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 weighting; Step G: Establish a TOPSIS evaluation model based on comprehensive weighting of game theory and calculate the resilience index value for flood disaster response; Step H: Use the flood disaster response resilience index value to obtain the flood disaster response resilience level, thereby completing the urban flood response resilience assessment task.

2. The urban flood resilience assessment method based on the HVEDR model according to claim 1 is characterized by: Step A, constructing the HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defense and resilience, where hazard susceptibility refers to urban flood disasters caused by precipitation drive, total rainfall, precipitation concentration and precipitation coverage; vulnerability refers to the damage and adverse effects suffered by systems, regions and societies when exposed to flood disasters; exposure refers to the number and value of population, property, economy, farmland and infrastructure exposed to natural disasters; defense refers to the implementation of engineering and non-engineering measures before and during flood disasters to reduce the possibility of disasters and mitigate the impact on human life, property, society and the environment; resilience refers to the ability of a region to quickly resume normal operations after a flood disaster.

3. The urban flood resilience assessment method based on the HVEDR model according to claim 2 is characterized by: Step B: Establish a hierarchical model based on the HVEDR model and according to the factors affecting flood disasters and their mutual attribute relationships, wherein the hierarchical model includes a target layer, a criterion layer and an indicator layer, wherein the target layer is the flood disaster resilience index, the criterion layer is the hazard causality, vulnerability, exposure, defense and recovery, and the indicator layer is the specific evaluation indicators.

4. The urban flood resilience assessment method based on the HVEDR model according to claim 3 is characterized by: In step C, based on the hierarchical structure model, the importance of the criterion layer and the indicator layer are compared by scaling method to obtain the hierarchical scale, and then the element importance judgment matrix is ​​constructed according to the hierarchical scale, wherein the element importance judgment matrix is ​​used to compare the importance of each element in the indicator layer with each criterion in the criterion layer. The specific element importance judgment matrix is ​​shown in formula (1). Among them, a ij is the judgment value obtained by comparison, a ji for a ij The reciprocal of .

5. The urban flood resilience assessment method based on the HVEDR model according to claim 4 is characterized by: Step E: Based on the HVEDR model and using the entropy weight method, the evaluation index judgment matrix is ​​constructed, and then the evaluation index entropy weight is calculated using the evaluation index judgment matrix. The specific steps are as follows: Step E1: Based on the HVEDR model and using the entropy weight method, the evaluation index judgment matrix is ​​constructed. Specifically, x ij is the value of the jth evaluation index of the i-th evaluation object, then the evaluation index judgment matrix is ​​(x ij ) m×n , where m is the evaluation object; Step E2, using the evaluation index judgment matrix to calculate the evaluation index entropy weight, the specific steps are as follows: Step E21, calculate the entropy value of the evaluation index, as shown in formula (7), Among them, e j is the entropy value of the evaluation index, p ij is the proportion of the i-th evaluation object under the j-th evaluation indicator; Step E22, calculate the information entropy difference coefficient, as shown in formula (8), d j =1-e j (8) Among them, d j is the information entropy difference coefficient; Step E23, calculate the entropy weight of the evaluation index, as shown in formula (9), Among them, w j is the evaluation index entropy weight.

6. The urban flood resilience assessment method based on the HVEDR model according to claim 5 is characterized by: Step F, using 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 weighting, where the game theory combined weight method specifically performs linear fitting on the weights obtained by different methods. The specific steps are as follows: Step F1, use different weighting methods to obtain the weight vector set w k ={w k1 ,w k2 ,…,w km } and calculate the linear combination of weight vectors, as shown in formula (10), Among them, w is the matrix composed of weight vectors obtained by different weighting methods, α k To optimize the linear combination coefficient, w k To obtain a set of weight vectors using different weighting methods, w k The transposed vector of Step F2, using game theory to introduce different weight vectors into the agreement and compromise and optimizing the linear combination coefficient α k Calculate w and w k Minimum deviation target Then the matrix differential property will minimize the deviation target The optimization is the first-order derivative condition, as shown in formula (11), Among them, w i for w and w k The eigenvector when the minimum deviation target condition is met, w i The transposed vector of Step F3, the linear combination coefficient α k Normalization, as shown in formula (12), Among them, α′ k is the linear combination coefficient of the kth 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 weighting, as shown in formula (13), Among them, w′ is the comprehensive weighting of game theory.

7. The urban flood resilience assessment method based on the HVEDR model according to claim 6 is characterized by: Step G: Based on the comprehensive weighting of game theory, a TOPSIS evaluation model is established and the flood disaster resilience index is calculated. The TOPSIS evaluation model uses the close distance between the evaluation object and the target to rank and determine the quality of the evaluation object. The specific steps are as follows: Step G1, determining the evaluation criteria and evaluation weights, specifically determining the evaluation criteria of the evaluation plan and assigning evaluation weights to each evaluation criterion; Step G2, standardize the judgment matrix, specifically use the original matrix to obtain the dimensionless decision matrix Z = (z ij ) m×n , as shown in formula (14), Among them, Z ij is the value of row i and column j in the dimensionless decision matrix Z; Step G3, construct the weighted decision matrix V, as shown in formula (15), v ij =w i ·z ij (15) Among them, v ij 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 based on the maximum and minimum values ​​of each evaluation criterion. and negative ideal solutions As shown in formula (16), Step G5, calculate the similarity, specifically by calculating the distance between each solution and the positive ideal solution and the negative ideal solution to obtain the solution and the positive ideal solution. and negative ideal solutions The similarity of is shown in formula (17). in, For the solution and the ideal solution The similarity of The solution and negative ideal solution similarity; Step G6, calculate the flood disaster resilience index value, specifically based on the scheme and the positive ideal solution and negative ideal solutions The flood disaster resilience index value is calculated based on the similarity of , as shown in formula (18): Among them, G j is the resilience index value for flood disaster response, where the resilience index value for flood disaster response G j The closer it is to 1, the better the evaluation result, and vice versa.

8. The urban flood resilience assessment method based on the HVEDR model according to claim 7 is characterized by: Step H: Use the flood disaster resilience index value to obtain the flood disaster resilience level, thereby completing the urban flood disaster resilience assessment task. Specifically, the K-means algorithm in cluster analysis is used to divide the flood disaster resilience index value into high resilience, relatively high resilience, medium resilience, relatively low resilience and low resilience flood disaster resilience levels.

9. An urban flood resilience assessment system based on the HVEDR model, wherein the specific assessment process of the urban flood resilience assessment system is based on the urban flood resilience assessment method according to any one of claims 1 to 8, characterized in that: It includes an HVEDR model construction module, a hierarchical 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 classification module. The HVEDR model construction module is used to construct an HVEDR model using the evaluation dimensions of hazard susceptibility, vulnerability, exposure, defense, and recovery; The hierarchical structure model building module is used to build a hierarchical structure model based on the HVEDR model and according to the factors affecting flood disasters and the attribute relationships between them; The importance comparison module is used to compare the importance of the criterion layer and the indicator layer based on the hierarchical structure model and adopt the scaling method to obtain the hierarchical scale, and then construct the 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 perform consistency check on the element weights; The entropy weight calculation module is used to construct an evaluation index judgment matrix based on the HVEDR model and adopt the entropy weight method, and then use the evaluation index judgment matrix to calculate the evaluation index entropy weight; 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 weighting; The flood disaster response resilience index value calculation module is used to establish a TOPSIS evaluation model based on comprehensive weighting of game theory and calculate the flood disaster response resilience index value; The flood disaster response resilience level classification 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 assessment task.

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