Urban flood toughness evaluation method based on VIKOR-grey correlation analysis

Abstract

The invention provides an urban flood toughness evaluation method based on VIKOR-grey correlation analysis. The urban flood toughness evaluation method comprises the following steps: determining indexes and establishing an urban flood toughness evaluation system; original data of the to-be-evaluated city are collected respectively; determining the weight of the first-level index and the weight of the second-level index by using subjective and objective combined game combination weighting; and determining the city flood toughness level of each city based on a VIKOR-grey correlation analysis model. According to the method, the to-be-evaluated city flood toughness is evaluated by adopting the VIKOR-grey correlation analysis model, and the evaluation is beneficial to comprehensively and accurately understanding the flood toughness level condition of each city; the implementation of fine management of each city is facilitated, the city flexible construction is improved, and the capability of resisting risks is improved; managers can conveniently find weak spots and take targeted measures, and reference is provided for urban flood toughness evaluation in the future.

Description

Technical Field

[0001] This invention relates to the field of urban flood resilience assessment technology, specifically to an urban flood resilience assessment method based on VIKOR-grey relational analysis. Background Technology

[0002] In recent years, changes in regional hydrological cycles caused by global warming have exacerbated the frequency of extreme weather events such as torrential rains and floods. Coupled with the continuous acceleration of urbanization, urban flooding has become one of the major natural disasters facing major cities in my country. It not only causes huge economic losses to cities, but also seriously threatens urban social stability and ecological civilization construction. The dual impact of rapid urbanization and urban crisis has prompted mankind to find new ways to cope, and resilience assessment is an important part of urban disaster risk management.

[0003] Although existing technologies have proposed various methods for assessing urban flood resilience and achieved good application results, there is still a lack of comprehensive and systematic assessment methods for urban flood resilience. Furthermore, existing technologies have other shortcomings, which can be summarized as follows:

[0004] (1) There are some deficiencies in the evaluation system, such as the lack of research on the impact on nature.

[0005] (2) Most studies use a single method for weighting indicators. Although there are also studies that combine qualitative and quantitative methods, there are few related studies, which cannot comprehensively and systematically reflect the various indicator factors.

[0006] (3) The existing urban flood resilience assessment methods used in the research are relatively simple and few, and cannot reflect the flood resilience level of the assessed cities more comprehensively.

[0007] The relatively simplistic evaluation systems and methods present significant limitations in urban flood resilience assessment, leading to less than ideal results. Therefore, there is an urgent need for a comprehensive and systematic urban resilience assessment method that can consider various factors. This invention is proposed based on this practical need. Summary of the Invention

[0008] In view of the problems existing in the prior art, the objective of the present invention is to provide an urban flood resilience assessment method based on VIKOR-grey relational analysis, which aims to solve the problem of the single assessment system and evaluation method in the existing problems.

[0009] To achieve the above objectives, the present invention uses the following technical solutions:

[0010] A method for assessing urban flood resilience based on VIKOR-grey relational analysis includes the following steps:

[0011] S1: Based on literature review and Delphi method, identify the various indicator factors and construct an urban flood resilience assessment system based on VIKOR-grey relational analysis;

[0012] S2: The constructed evaluation indicators are subjectively and objectively weighted using the Analytic Network Method (ANP) and the Entropy Weight Method (EWM), respectively, and then the final combined weight of each indicator is determined through game theory combination weighting.

[0013] S3: Using the VIKOR-grey relational analysis model, the comprehensive evaluation value of each city to be evaluated is obtained.

[0014] Preferably, step S1 includes the following sub-steps:

[0015] S11: By referencing a large number of literatures related to urban flood resilience, and taking into account the actual situation of urban flood disaster response in China, the urban flood resilience assessment system studied is initially divided into four aspects: natural, economic, social and infrastructure, and secondary indicators are initially selected.

[0016] S12: Using the Delphi method, the preliminary urban flood resilience index system was made into an index screening questionnaire and sent to the expert group members to inquire about their classification, importance judgment and correction of the index system;

[0017] S13: Finally, collect the questionnaires and score the indicators from three aspects: concentration, coordination and dispersion.

[0018] S14: Determine the final urban flood resilience assessment index system based on the scoring results.

[0019] Preferably, step S13 specifically includes:

[0020]

[0021] in, This represents the concentration, where n represents the number of experts. This represents the score given by the i-th expert to the j-th indicator.

[0022]

[0023] in, Indicates the degree of dispersion.

[0024]

[0025] in, Indicates the degree of coordination.

[0026] Furthermore, when the calculated consistency score for a certain indicator is between 0 and 0.35, it means that the consistency of this indicator is relatively good, that is, the opinions of different experts are relatively consistent; when... When, it is considered important and should be retained; when... At times, it may be considered that the indicator is optional, and in such cases, a comprehensive evaluation based on actual data is necessary; when At that time, it was considered that the indicator was unimportant and should be discarded.

[0027] Preferably, the urban flood resilience assessment index system described in step S14 is as follows: the primary indicators include natural, economic, social, and infrastructure indicators; the secondary indicators include annual average temperature, annual average rainfall, green coverage rate of built-up areas, per capita GDP, fiscal expenditure on agriculture, forestry and water affairs, government general public budget revenue, total assets of industrial enterprises above designated size, the proportion of the tertiary industry in GDP, population density, social security and employment expenditure, per capita disposable income of urban residents, number of students enrolled in regular institutions of higher learning, emergency management capacity, per capita urban road area, length of drainage pipelines, number of medical beds, density of drainage pipelines in built-up areas, and sewage treatment capacity.

[0028] Preferably, step S2 includes the following sub-steps:

[0029] S21: Calculate the subjective weights of indicators at each level, take urban flood resilience as the control layer target, take natural, economic, social and infrastructure as the elements of the network layer, and input their respective secondary indicators and their influence relationships into the "SuperDecisions" software to form a network structure diagram.

[0030] S22: Construct the judgment matrix. Using an expert survey method and a 1-9 scale, determine the dominance of each pair of indicators. Input the judgment results into the "Super Decisions" software and verify whether the CR=CI / RI<0.1 condition of the comparison matrix is ​​true to avoid logical errors.

[0031] S23: After inputting all the decision matrices into the "Super Decisions" software according to the steps above, you will obtain the unweighted supermatrix, weighted supermatrix, and limit supermatrix. Finally, clicking the "Priority" command will give you the subjective weight results for each indicator. .

[0032] S24: Collect the raw data required for each secondary indicator in this study.

[0033] S25: Calculate the objective weights of each level of indicator. The formula for calculating the objective weights is as follows:

[0034]

[0035] in, This represents the raw data for the j-th indicator of the i-th evaluation object. This represents the initial evaluation matrix constructed from m objects and n indicators;

[0036]

[0037]

[0038] in, , These represent the maximum and minimum values ​​of the original data for the j-th indicator of the i-th evaluation object, respectively. , These represent the results of standardization for the positive and negative indicators within each secondary indicator;

[0039]

[0040]

[0041] Meanwhile, after the indicators are standardized, they need to be shifted to avoid division by zero. The shift range is generally 0.001.

[0042]

[0043]

[0044]

[0045] in, Indicates the variability of the indicator. Represents information entropy. And must meet , Indicates information entropy redundancy;

[0046]

[0047] in, This indicates the objective weight of each secondary indicator.

[0048] S26: Calculate the combined weights of each level of indicators. The formula for calculating the weights of the game combination is as follows:

[0049]

[0050] in, Indicates the number of weighting methods. This represents the weight vector of n indicators obtained by these methods. Describes the coefficients of a linear combination and , ;

[0051]

[0052]

[0053] in, Denotes the coefficients of the optimal linear combination. for The first derivative condition;

[0054]

[0055] The system of equations is as follows: A system of linear differential equations with optimal first-order derivative conditions;

[0056]

[0057] in, express The normalization result;

[0058]

[0059] in, This represents the combined weight of the game among the indicators.

[0060] Preferably, the calculation formula for the VIKOR-grey relational analysis model in step S3 is as follows:

[0061]

[0062]

[0063] in, Let j be the value of the evaluation index for the i-th evaluation object after standardization. This represents a set of positive indicators. This represents a set of negative indicators. This represents the ideal solution. Indicates the negative ideal solution;

[0064]

[0065]

[0066] in, , These represent the group benefit value and individual regret value calculated using the VIKOR method, respectively. Indicates the weight of the game combination;

[0067]

[0068]

[0069] in, This represents the resolution coefficient, which is typically taken as 0.5. , These represent the grey relational coefficients between the index value and the positive and negative ideal solutions, respectively.

[0070]

[0071]

[0072] Where m represents the total number of indicators, , These represent the grey relational degree between the index value and the positive and negative ideal solutions, respectively.

[0073]

[0074]

[0075] in, , These represent the group benefit value and individual regret value of the evaluated objects under grey relational analysis, respectively.

[0076]

[0077] in, This represents the decision-making mechanism coefficient, typically taken as 0.5. , , Let these represent the group benefit value, minimum group benefit value, and maximum group benefit value of the i-th evaluation object, respectively. , , Let these represent the individual regret value, minimum individual regret value, and maximum individual regret value of the i-th evaluation object, respectively. This represents the urban flood resilience level of the i-th evaluation object, and The smaller the value, the higher the level of urban flood resilience being assessed, and vice versa.

[0078] Compared to current technologies, the advantages of this invention are:

[0079] (1) This invention incorporates a new level of “nature” into the urban flood resilience assessment system, which enriches the perspective of previous research and can provide more references for future urban flood resilience assessment.

[0080] (2) In terms of the weighting method, the present invention selects a combination of subjective and objective game-based weighting. The combination of game-based weighting makes up for the shortcomings of single weighting and can find the consistency or difference between the weights obtained by different methods, that is, to achieve Nash equilibrium, so that the final weight result calculated is more accurate and usable.

[0081] (3) The present invention uses the VIKOR-grey relational analysis model to evaluate the city under study, which improves the singleness of the evaluation method in previous studies. It helps to have a comprehensive and accurate understanding of the flood resilience level of the city under evaluation, and makes it easier for managers to find weaknesses and take targeted measures. It is of great significance for improving the flood resilience of the city. Attached Figure Description

[0082] Figure 1 This is a flowchart illustrating the specific process of this invention;

[0083] Figure 2 This is a diagram of the urban flood resilience assessment index system of the present invention;

[0084] Figure 3 This is a network structure diagram of the present invention. Detailed Implementation

[0085] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention.

[0086] A method for assessing urban flood resilience based on VIKOR-grey relational analysis, such as Figure 1 As shown, it includes the following steps:

[0087] S1: Based on a literature review, primary and secondary indicators for urban flood resilience assessment were initially selected; questionnaires were distributed to relevant experts to classify, assess, and refine the selected indicators; the collected questionnaires were compiled, and the final evaluation indicator system was determined based on concentration, consistency, and dispersion; this was implemented through the following steps:

[0088] S11: By referring to a large number of literatures related to urban flood resilience, and taking into account the actual situation of urban flood disaster response in China, the urban flood resilience assessment system studied is initially divided into four aspects: natural, economic, social and infrastructure. At the same time, the secondary indicators are initially selected, as shown in Table 2.

[0089] S12: Using the Delphi method, the preliminary urban flood resilience index system was made into an index screening questionnaire and sent to members of the expert group in the field of urban flood resilience assessment to inquire about their classification, importance judgment and correction of the index system. The expert group profile is shown in Table 1.

[0090] Table 1. Overview of the Expert Group

[0091]

[0092] S13: Finally, the questionnaires were collected, and the scores were statistically analyzed based on three aspects: concentration, coordination, and dispersion. The specific calculation process included:

[0093]

[0094] in, This represents the concentration, where n represents the number of experts. This represents the score given by the i-th expert to the j-th indicator.

[0095]

[0096] in, Indicates the degree of dispersion.

[0097]

[0098] in, Indicates the degree of coordination.

[0099] Furthermore, when the calculated consistency score for a certain indicator is between 0 and 0.35, it means that the consistency of this indicator is relatively good, that is, the opinions of different experts are relatively consistent; when... When, it is considered important and should be retained; when... At times, it may be considered that the indicator is optional, and in such cases, a comprehensive evaluation based on actual data is necessary; when At that time, it was considered that the indicator was unimportant and should be discarded.

[0100] S14: Determine the final urban flood resilience assessment index system based on the scoring results. The final assessment results are shown in Table 2.

[0101] Table 2. Centralization, Dispersion, and Coherence of Preliminary Screening Indicators

[0102]

[0103] S141: As described in step S14, the final urban flood resilience assessment index system is as follows: the primary indicators include natural, economic, social, and infrastructure indicators; the secondary indicators include annual average temperature, annual average rainfall, green coverage rate of built-up areas, GDP per capita, fiscal expenditure on agriculture, forestry and water affairs, government general public budget revenue, total assets of industrial enterprises above designated size, the proportion of the tertiary industry in GDP, population density, social security and employment expenditure, per capita disposable income of urban residents, number of students enrolled in regular institutions of higher learning, emergency management capacity, per capita urban road area, length of drainage pipelines, number of medical beds, density of drainage pipes in built-up areas, and sewage treatment capacity.

[0104] S2: Subjectively and objectively weight the constructed evaluation indicators using Analytic Network Analysis (ANP) and Entropy Weight Method (EWM) respectively, and then determine the final combined weight of each indicator through game theory combination weighting. This is implemented in detail through the following steps:

[0105] S21: Calculate the subjective weights of each level of indicators, invite n relevant experts to score the interrelationships among the 18 constructed evaluation indicators, use urban flood resilience as the control layer objective, and use natural, economic, social, and infrastructure elements as the network layer elements. Input their respective secondary indicators and their influencing relationships into the "Super Decisions" software to form a network structure diagram, such as... Figure 3 As shown;

[0106] S22: Construct the judgment matrix. Using an expert survey method and a 1-9 scale, determine the dominance of each pair of indicators. Input the judgment results into the "Super Decisions" software and check whether the judgment matrix CR=CI / RI<0.1 is valid. If not, modify the judgment matrix of the secondary evaluation indicators until the check is valid.

[0107] S23: After inputting all the decision matrices into the "Super Decisions" software according to the steps above, you will obtain the unweighted supermatrix, weighted supermatrix, and limit supermatrix. Finally, clicking the "Priority" command will give you the subjective weight results for each indicator. ;

[0108] S24: Collect the raw data required for each secondary indicator in this study, as shown in Table 3;

[0109] Table 3 Data Sources

[0110]

[0111] S25: Calculate the objective weights of each level of indicator. The steps for calculating the objective weights are as follows:

[0112] First, based on the collected raw data of the secondary indicators, an initial evaluation matrix is ​​constructed, as shown below:

[0113]

[0114] in, This represents the raw data for the j-th indicator of the i-th evaluation object. This represents the initial evaluation matrix constructed from m objects and n indicators;

[0115] The positive and negative indicators are then standardized separately using the following steps, with the specific formulas shown below:

[0116]

[0117]

[0118] in, , These represent the maximum and minimum values ​​of the original data for the j-th indicator of the i-th evaluation object, respectively. , These represent the results of standardization for the positive and negative indicators within each secondary indicator;

[0119] Furthermore, after standardizing the indicators, a shift is required to avoid division by zero. The shift range is typically 0.001, expressed by the formula:

[0120]

[0121]

[0122] Next, the variability, information entropy, and information entropy redundancy of the indicators are calculated.

[0123]

[0124]

[0125]

[0126] in, Indicates the variability of the indicator. Represents information entropy. And must meet , Indicates information entropy redundancy;

[0127] Finally, after normalizing the information entropy redundancy, the objective weights of each level of indicator can be obtained, expressed by the formula:

[0128]

[0129] in, This indicates the objective weight of each secondary indicator.

[0130] S26: Calculate the combined weights of each level of indicators. The formula for calculating the weights of the game combination is as follows:

[0131]

[0132] in, Indicates the number of weighting methods. This represents the weight vector of n indicators obtained by these methods. Describes the coefficients of a linear combination and , ;

[0133]

[0134]

[0135] in, Denotes the coefficients of the optimal linear combination. for The first derivative condition;

[0136]

[0137] The system of equations is as follows: A system of linear differential equations with optimal first-order derivative conditions;

[0138] Next, normalization is performed, and the formula is expressed as:

[0139]

[0140] in, express The normalization result;

[0141] Finally, multiply the coefficients obtained from the two weighting methods by their respective weights and then add them together to obtain the final combined weight, expressed by the formula:

[0142]

[0143] in, This represents the combined weight of the game among the indicators.

[0144] S3: Input the combined weights of the obtained evaluation indicators into the VIKOR-grey relational analysis model to obtain the urban flood resilience level of each city to be evaluated. This can be achieved through the following steps:

[0145] First, calculate the positive and negative ideal solutions for the standardized index, expressed by the following formula:

[0146]

[0147]

[0148] in, Let j be the value of the evaluation index for the i-th evaluation object after standardization. This represents a set of positive indicators. This represents a set of negative indicators. This represents the ideal solution. Indicates the negative ideal solution;

[0149] Based on the obtained game combination weights Then calculate the group benefit value obtained under the VIKOR method. and individual regret value The formula is expressed as:

[0150]

[0151]

[0152] Next, the grey relational coefficients between the index and the positive and negative ideal solutions are calculated, expressed by the formula:

[0153]

[0154]

[0155] in, This represents the resolution coefficient, which is typically taken as 0.5. , These represent the grey relational coefficients between the index value and the positive and negative ideal solutions, respectively.

[0156] Then, the grey relational degree is calculated based on the obtained grey relational coefficients, and the formula is expressed as:

[0157]

[0158]

[0159] Where m represents the total number of indicators, , These represent the grey relational degree between the index value and the positive and negative ideal solutions, respectively.

[0160] Based on the grey relational coefficient and grey relational degree, calculate the group benefit value of the indicator under grey relational analysis. and individual regret value The formula is expressed as:

[0161]

[0162]

[0163] Finally, by combining the calculated VIKOR with the group benefit value and individual regret value obtained from grey relational analysis, the final comprehensive evaluation value, namely the urban flood resilience level, can be obtained, expressed by the formula:

[0164]

[0165] in, This represents the decision-making mechanism coefficient, typically taken as 0.5. , , Let these represent the group benefit value, minimum group benefit value, and maximum group benefit value of the i-th evaluation object, respectively. , , Let these represent the individual regret value, minimum individual regret value, and maximum individual regret value of the i-th evaluation object, respectively. This represents the urban flood resilience level of the i-th evaluation object, and The smaller the value, the higher the level of urban flood resilience being assessed, and vice versa.

[0166] Parts not specifically described in this invention specification are prior art known to those skilled in the art.

Claims

1. A VIKOR-gray correlation analysis-based urban flood resilience assessment method, characterized in that, Comprising the following steps: S1: Based on literature review and Delphi method, determine each index factor, and construct the urban flood resilience evaluation system based on VIKOR-gray correlation analysis; S2: Use network analysis method (ANP) and entropy weight method (EWM) to subjectively and objectively weight the evaluation index constructed, and then determine the final combined weight of each index through game combination weighting; S3: Obtain the comprehensive evaluation value of each city to be evaluated through the VIKOR-gray correlation analysis model.

2. The urban flood resilience assessment method based on VIKOR-gray correlation analysis according to claim 1, characterized in that Step S1 includes the following sub-steps: S11: By referring to a large number of literatures related to urban flood resilience, and considering the actual situation of Chinese cities in coping with flood disasters, the urban flood resilience evaluation system is initially divided into four aspects of nature, economy, society and infrastructure, and the secondary indicators are initially screened out; S12: With the help of Delphi method, the initially constructed urban flood resilience index system is made into an index screening questionnaire, which is sent to the members of the expert group to inquire about their classification of the index system, importance judgment and correction, etc.; S13: Finally, the questionnaire is recovered, and the index scoring statistics are carried out from the aspects of concentration, coordination and dispersion; S14: Determine the final urban flood resilience evaluation index system according to the scoring results.

3. The urban flood resilience assessment method based on VIKOR-gray correlation analysis according to claim 2, characterized in that The specific formula and requirements of concentration, coordination and dispersion in step S13 are: ； wherein, denotes the concentration, n denotes the number of experts, denotes the score of the i-th expert on the j-th indicator; ； wherein represents the dispersion; ； wherein denotes the coordination degree; And when the coordination value of a certain index is between 0-0.35, it means that the coordination of the index is better, that is, the opinions of different experts are more consistent; when , it is considered that the index is important and should be retained; when , it is considered that the index is optional, at which time the actual data needs to be comprehensively considered; when , it is considered that the index is not important and should be discarded.

4. The urban flood resilience assessment method based on VIKOR-gray correlation analysis according to claim 2, characterized in that The urban flood resilience evaluation index system in step S14 is specifically: the first-level indicators include nature, economy, society and infrastructure; the second-level indicators include annual average temperature, annual average rainfall, built-up area green coverage rate, per capita GDP, agriculture, forestry and water affairs financial expenditure, government general public budget revenue, total assets of large-scale industrial enterprises, proportion of tertiary industry in GDP, population density, social security and employment expenditure, per capita disposable income of urban residents, number of students in regular institutions of higher learning, emergency management capacity, per capita urban road area, drainage pipe length, medical bed number, built-up area drainage pipe density, sewage treatment capacity.

5. The urban flood resilience assessment method based on VIKOR-gray correlation analysis according to claim 1, characterized in that Step S2 includes the following sub-steps: S21: Calculate the subjective weight of each level index, take urban flood resilience as the control layer target, take nature, economy, society and infrastructure as the element group of network layer, and input the secondary indicators and their influence relationship thereof into "SuperDecisions" software to form a network structure diagram; S22: Construct a judgment matrix. Through expert investigation method, use 1-9 scale method to determine the dominance of two-by-two comparison between indexes, input the judgment results into "Super Decisions" software, and test whether CR=CI / RI<0.1 of the comparison matrix is established to avoid logical errors; S23: After all the judgment matrices are input into the "Super Decisions" software according to the above steps, the unweighted supermatrix, the weighted supermatrix and the limiting supermatrix can be obtained. Finally, the subjective weight results of each index can be obtained by clicking the "priority" command ; S24: Collect the original data required by each secondary index in this study; S25: Calculate the objective weight of each level index, and the objective weight calculation formula is: ； wherein, represents the original data of the jth index of the ith evaluation object, represents an initial evaluation matrix constructed with m objects and n indices; ； ； wherein, , respectively represent the maximum value and the minimum value of the original data in the jth index of the ith evaluation object, , respectively represent the results of the standardization of the positive index and the negative index among the secondary indices. ； ； Meanwhile, after standardizing the indexes, in order to avoid zero divisor, translation is needed, and the translation amplitude is generally 0.001; ； ； ； wherein, denotes the index variability, denotes the information entropy, and needs to satisfy , denotes the information entropy redundancy; ； wherein, represents the objective weight of each secondary index; S26: Calculate the combined weight of each level index, and the calculation formula of game combination weighting is: ； wherein, denotes the number of empowerment methods, denotes the weight vector of the n indices obtained by these methods, denotes the linear combination coefficients and , ; ； ； wherein denotes the optimal linear combination coefficients, is the first derivative condition; ； where the system of equations is Linear differential equations system of optimal first derivative conditions ； wherein represents the normalized result of ； wherein, denotes the game combination weight of each index.

6. The urban flood resilience assessment method based on VIKOR-gray correlation analysis according to claim 1, characterized in that, The VIKOR-grey correlation analysis model calculation formula of step S3 is as follows: ； ； wherein, is a value of the jth evaluation index of the ith evaluation object after standardization processing, denotes a set of positive indexes, denotes a set of negative indexes, denotes a positive ideal solution, denotes a negative ideal solution; ； ； wherein, , denote the group utility value and individual regret value, respectively, calculated using the VIKOR method, denote the game combination weights; ； ； wherein, denotes the resolution coefficient, generally taken as 0.5, , respectively denote the index value and the grey correlation coefficient of the positive and negative ideal schemes. ； ； Wherein, m represents the total number of indexes, , respectively represent the grey correlation degree of index value and positive and negative ideal scheme. ； ； wherein, , respectively represent the group benefit value and the individual regret value of the evaluated object under grey correlation analysis. ； wherein, represents the decision mechanism coefficient, generally taken as 0.5, , , respectively represent the group benefit value, the minimum group benefit value and the maximum group benefit value of the i-th evaluation object, , , respectively represent the individual regret value, the minimum individual regret value and the maximum individual regret value of the i-th evaluation object, represents the urban flood resilience level of the i-th evaluation object, and The smaller the value is, the higher the evaluated urban flood resilience level is, and vice versa.

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