Urban anti-waterlogging toughness evaluation method

Abstract

The invention provides an urban anti-waterlogging toughness evaluation method, which combines a pressure-state-response model with society, economy, environment and infrastructure, and constructs an urban anti-waterlogging toughness evaluation index system table. The subjective weight and the objective weight are combined, the final weight is determined through a combined weighting method, the objective weight is determined through an entropy weight method, and the subjective weight is determined through an analytic hierarchy process; in consideration of fuzziness and randomness of dimension division and index selection in an urban anti-waterlogging toughness evaluation process, an evaluation model is constructed by using a cloud model, a membership matrix is obtained by using a normal cloud model, and calculation is performed by combining a corresponding weight value to obtain an urban anti-waterlogging toughness comprehensive certainty degree and a grade characteristic value; and finally determining an urban anti-waterlogging toughness evaluation grade. According to the method, numerous uncertain factors in the urban anti-waterlogging toughness evaluation process are converted into quantitative research, quantitative evaluation of the urban anti-waterlogging toughness level is effectively achieved, and a reliable method is provided for urban anti-waterlogging toughness evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster prevention and reduction, and in particular to a method for evaluating urban flood resistance resilience. Background Art

[0002] In the context of global warming, extreme precipitation events occur frequently. We should implement urban renewal and transformation, flood control and drainage, build resilient cities, and elevate resilience construction to sustainable development. Scientifically measuring the level of urban flood resilience and accurately identifying the key factors of urban flood resilience can not only improve urban resilience, but also provide theoretical support for improving urban flood management decisions. It is also of great significance to satisfy people's yearning for a better life.

[0003] The urban flood resilience assessment method is used to measure the ability of the urban system to adapt to changes, self-learn, reduce losses, and maintain the flood resistance and virtuous cycle of the urban system when it is disturbed and destroyed by the outside world. At present, there are many methods for assessing urban flood resilience, and the main methods can be roughly summarized into four types: Method 1: Hierarchy Analysis Method (AHP), that is, the indicators are layered, the importance of each indicator in the same layer is determined through expert consultation, the weight of the indicator is calculated, and finally the final resilience value is obtained through progressive weighted calculation. Method 2: TOPSIS method is a ranking evaluation method that is close to the ideal solution. The urban flood resilience level is ranked by detecting the distance between the evaluation indicator and the optimal solution and the worst solution. Method 3: GIS spatial analysis method, with the help of GIS technology, the information data in the urban remote sensing image is extracted to analyze and evaluate the urban flood resilience from a spatial perspective. Method 4: Entropy method, the objective weight is determined according to the size of the information entropy value of the evaluation indicator. The smaller the information entropy reflected by the urban flood resilience evaluation indicator, the more information it provides, and the greater the weight.

[0004] Method 1 can only evaluate relevant indicators with the help of the experience of experts or scholars. It has many qualitative components and less quantitative data, and the weight calculation is highly subjective, which makes it difficult to evaluate the true level of urban flood resilience in terms of data support and scientificity. Method 2 Since the Euclidean distance formula used in the TOPSIS method is symmetrical, it cannot evaluate the situation of points on the perpendicular midline of the line connecting the positive ideal point and the negative ideal point, and cannot guarantee the accuracy and rationality of the evaluation results. Method 3 Since the higher the data accuracy, the smaller the pixel grid is required, the data accuracy is limited by the pixel grid size, and it is difficult to obtain high-precision data for small areas. Method 4 determines the indicator weight according to the degree of variation of each indicator value, and can use mathematical statistics to scientifically analyze urban flood resilience, but lacks the integration of subjective perception, resulting in the evaluation results being too different from expectations. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the existing urban flood resistance resilience assessment method has the problem that the indicator system construction ignores the causal correlation and process dynamics, the weight calculation is single, and the uncertainty and ambiguity in the indicator selection and dimensional division in the assessment process are ignored.

[0006] In order to solve the above technical problems, the technical solution of the present invention provides a method for evaluating urban flood resistance, comprising the following steps:

[0007] Based on the pressure-state-response model, the social, economic, environmental and infrastructure are combined as the criterion layer, and the corresponding specific changes in the social, economic, environmental and infrastructure are analyzed as the indicator layer to construct an urban flood resilience assessment indicator system table;

[0008] According to the current characteristics of the city and the connotation of resilience, with reference to relevant literature and expert opinions, combined with field survey data, the grade standards of each indicator in the indicator layer are established, and the evaluation grades of the corresponding indicators in the indicator layer are divided;

[0009] The importance of each indicator in the criterion layer and the indicator layer with evaluation levels is compared and assigned in pairs, the relative importance of each indicator to the upper level indicator is quantified, the judgment matrix is ​​constructed, the maximum characteristic root of the judgment matrix and its corresponding characteristic vector are solved, and the maximum characteristic root is tested for consistency according to the consistency verification formula and the average random consistency indicator value table. For the maximum characteristic root that fails the consistency, the corresponding judgment matrix is ​​readjusted until the verification is passed and the subjective weight is determined;

[0010] The extreme value method is used to standardize each indicator of the indicator layer with evaluation levels, and the entropy weight method is used to calculate the weight vector of resilience evaluation indicators as the objective weight;

[0011] According to the subjective weight and objective weight, the weight of resilience assessment index is determined by combining weights;

[0012] According to each indicator in the indicator layer with evaluation levels, the expectation, entropy and super entropy in the cloud model are determined, by generating a first normal random number with a mean of entropy and a standard deviation of super entropy, generating a second normal random number with a mean of expectation and a standard deviation of entropy, and solving multiple memberships of the second normal random number to the qualitative concept;

[0013] According to the weights and membership of resilience assessment indicators, the comprehensive membership of urban flood resilience is calculated, and the characteristic value of the assessment grade is calculated to determine the urban flood resilience grade.

[0014] Preferably, the construction judgment matrix adopts a nine-level scale of relative importance of factors.

[0015] Preferably, solving the maximum eigenvalue of the judgment matrix and its corresponding eigenvector comprises the following steps:

[0016] The product of each row of elements in the judgment matrix is ​​calculated, and the nth root of the product of each row of elements is calculated, the vector after the nth root is normalized, the maximum characteristic root is calculated based on the normalized data, and the characteristic vector is determined based on the maximum characteristic root.

[0017] Preferably, the consistency verification formula is as follows:

[0018]

[0019] Among them, RI is the average random consistency index, CR is the random consistency ratio, CI is the consistency index, and the consistency index CI formula is as follows:

[0020]

[0021] Among them, λ max is the maximum characteristic root of the judgment matrix, and n is the order in the average random consistency index value table.

[0022] Preferably, for the maximum characteristic root that fails the consistency, it is determined whether CR=CI / RI<0.10, and if so, it is manually determined that the consistency passes.

[0023] Preferably, a cloud chart is formed based on the multiple second normal random numbers and the multiple membership degrees, and the evaluation level of the indicator is displayed on the cloud chart.

[0024] The technical solution of the present invention proposes a method for evaluating urban flood resilience, which considers the causal correlation and process dynamics of the urban flood resilience index system, combines the pressure-state-response model with society, economy, environment, and infrastructure, and constructs an urban flood resilience evaluation index system table; combines subjective weights with objective weights, and uses a combined weighting method to determine the final weights, wherein the entropy weight method determines the objective weights, and the hierarchical analysis method determines the subjective weights; considering the fuzziness and randomness of dimensional division and indicator selection in the process of urban flood resilience evaluation, the cloud model is used to construct an evaluation model, and the normal cloud model is used to obtain the membership matrix and the corresponding weight values ​​are combined to calculate the comprehensive determination degree and grade characteristic value of urban flood resilience, and finally determine the urban flood resilience evaluation grade. The present invention converts many uncertain factors in the process of urban flood resilience evaluation into quantitative research, effectively realizes quantitative evaluation of urban flood resilience level, and provides a reliable method for urban flood resilience evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for evaluating urban flood resilience provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0027] The embodiment of the present invention provides a method for evaluating urban flood resilience, which can convert many uncertain factors in the process of evaluating urban flood resilience into quantitative research, effectively realize quantitative evaluation of urban flood resilience level, and provide a reliable method for evaluating urban flood resilience, including the following steps:

[0028] S1. Based on the Pressure-State-Response (PSR) model, an urban flood resilience assessment index system is constructed by comprehensively considering the basic components of the city (society, economy, environment, and infrastructure). The indicators for urban flood resilience assessment are divided into five levels. The steps are as follows:

[0029] Since the Pressure-State-Response (PSR) model has the systemic idea of ​​causal correlation and process dynamics, it is based on the study of urban flood resilience process, and the basic components of the city are based on the study of the state. The two are organically combined to construct an urban flood resilience assessment index system:

[0030] The pressure dimension describes why the system changes. Human energy consumption, urbanization and other activities change the environment, and the environment exerts pressure on humans in the form of climate warming and frequent extreme rainfall, forming flood disaster risks. The state dimension describes what changes have occurred in the system. In the face of flood damage, urban social, economic, ecological, and infrastructure subsystems resist and show real-time status. The response dimension describes how the system responds. The city takes a series of measures to respond, and through summary and learning, it responds to the next impact. The urban flood resilience index system is shown in Table 1.

[0031] Table 1 Urban flood resilience assessment index system

[0032]

[0033]

[0034] The standards for setting the urban flood resilience assessment index level are as follows:

[0035] Based on the current characteristics of the city and the connotation of resilience, with reference to relevant literature and expert opinions, and combined with field survey data, the standards for each indicator of urban flood resistance resilience were determined and divided into five assessment levels: low resilience (Ⅰ), relatively low resilience (Ⅱ), moderate resilience (Ⅲ), relatively high resilience (Ⅳ), and high resilience (V).

[0036] S2, calculate the subjective weights using the analytic hierarchy process.

[0037] The calculation of the analytic hierarchy process to determine the toughness assessment index weight vector is as follows:

[0038] In order to determine the importance of each indicator to the overall target layer, experts in related fields are invited to compare and assign values ​​to the importance of each indicator in the criterion layer and the indicator layer. The relative importance ranking is obtained by calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, and the weight of each indicator is finally determined through consistency test and hierarchical total ranking test. The specific steps are as follows:

[0039] S21, establishing a hierarchical structure.

[0040] S22, construct a judgment matrix, use the 9-level scaling method, compare each factor pairwise, and quantify the relative importance of each indicator to the previous level indicator.

[0041] The judgment matrix is:

[0042]

[0043] Among them, b ij is the value of the pairwise comparison of the importance of the i-row index relative to the j-column index, b ij >0,b ii =1,

[0044] The nine-level scale is shown in Table 2.

[0045] Table 2 Nine-level scale of relative importance of factors

[0046]

[0047] S23, solve the judgment matrix, and use the square root method to find the maximum eigenvalue and its corresponding eigenvector from the judgment matrix. Let the maximum eigenvalue of B be λ max , the corresponding eigenvector is W, then AW=λ max W, the specific settlement model is as follows:

[0048] Calculate the product of each row of the judgment matrix A to get M i :

[0049]

[0050] Among them, i=1,2,…,n, j=1,2,…,n.

[0051] Calculate M i The nth root of

[0052]

[0053] Among them, i=1,2,…,n.

[0054] Pair Vector Normalized to get:

[0055]

[0056]

[0057] in, is the subjective weight of the indicator, i=1,2,…,n, W is the characteristic vector of B;

[0058] Calculate the maximum eigenvalue of the judgment matrix:

[0059]

[0060] Among them, λ max is the maximum eigenvalue of B;

[0061] (BW) i is the i-th element of the vector BW:

[0062]

[0063] S24, performing consistency check on the judgment matrix.

[0064]

[0065] Among them, CI is the consistency index, RI is the average random consistency index, and CR is the random consistency ratio;

[0066] The CI can be given by:

[0067]

[0068] Among them, λ max is the maximum eigenvalue of the judgment matrix.

[0069] In order to measure whether the judgment matrices of different orders have satisfactory consistency, it is necessary to introduce the value of the average random consistency index RI of the judgment matrix. The RI values ​​of the 1st to 9th order judgment matrices are shown in Table 3. When CR = CI / RI < 0.10, the artificial judgment matrix has satisfactory consistency. Otherwise, the judgment matrix needs to be adjusted to make it have satisfactory consistency.

[0070] Table 3 Average random consistency index RI value

[0071] Order n 1 2 3 4 5 6 7 8 9 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0072] S3, the extreme value method is used to standardize the evaluation index data, and the entropy weight method is used to calculate the objective weight.

[0073] The formulas for standardizing the evaluation index data using the extreme value method are as follows:

[0074] Positive indicator normalization:

[0075]

[0076] Negative indicator normalization:

[0077]

[0078] Among them, x ij is the original data of the ith annual indicator under the jth indicator, Y ij It is the standardized data of the i-th annual indicator under the j-th indicator, i = 1, 2, ..., n, n is the number of indicators, j = 1, 2, ..., m, m is the number of years.

[0079] The entropy weight method determines the weight vector of resilience assessment indicators:

[0080] The entropy weight method is based on the discrete degree of data and objectively reflects the influence of each indicator in the sample data on the evaluation results. The specific steps are as follows:

[0081]

[0082] Among them, p ij For x ij Probability of occurrence.

[0083]

[0084] Among them, s i is the entropy of the indicator.

[0085] d i =1-s i (14)

[0086] Among them, d iis the redundancy of each indicator.

[0087]

[0088] In the formula, is the objective weight of each indicator,

[0089] S4, use combined weighting to determine the weights of resilience assessment indicators.

[0090] The combined weights of urban flood resilience indicators are as follows:

[0091] According to the idea of ​​combined weighting method, the hierarchical analysis method and entropy weight method are combined to make the weight take into account the objective influence and relative importance of the indicators on the evaluation results, so as to improve the reliability and scientificity of the evaluation results. The specific formula is:

[0092]

[0093] in, is the combined weight, is the weight obtained by the hierarchical analysis method, is the weight obtained by the entropy weight method.

[0094] S5, determine the membership using the forward cloud generator of the normal cloud model.

[0095] The cloud model includes three eigenvalues: expectation (Ex), entropy (En), and hyperentropy (He). Ex represents the center of gravity of the cloud, En represents the randomness of the appearance of cloud droplets, and He is the entropy of entropy, representing the condensation degree of cloud droplets. The specific solution model is as follows:

[0096]

[0097] He=k (19)

[0098] Where: Z max , Z min are the upper and lower limits of the evaluation standard grade range respectively, k is a constant, which is taken as 0.5 based on experience.

[0099] The forward cloud generator is based on the three characteristic values ​​of expectation (Ex), entropy (En), and super entropy (He):

[0100] 1) Generate a normal random number E' with mean En and standard deviation He n ;

[0101] 2) Generate a normal random number c with mean Ex and standard deviation En i ;

[0102] 3) Solve for c i Membership degree μ to qualitative concepti :

[0103]

[0104] 4) Repeat steps 1) to 3) N times to obtain N cloud droplets (C i , μ i ) form a cloud map, which displays the evaluation levels of the indicators in the indicator layer, so that the evaluation levels of the indicators can be seen intuitively.

[0105] S6. Use the weight and membership matrix to obtain the comprehensive membership of each indicator corresponding to the urban flood resilience assessment level, use the level eigenvalue to quantify the assessment results, and determine the flood resilience assessment level.

[0106] Calculate the comprehensive membership of flood resistance resilience:

[0107]

[0108] Where Kl is the degree of certainty of the evaluation object with respect to the lth level, l = (1, 2, ..., r), r = (1, 2, 3, 4, 5); is the weight of the i-th indicator; μ ij is the degree of certainty of the ith indicator on the lth level.

[0109] Calculate the evaluation level eigenvalues:

[0110]

[0111] Where: K is the level characteristic value; r is the total number of levels.

[0112] The embodiment of the present invention provides a method for evaluating urban flood resilience, which considers the causal correlation and process dynamics of the urban flood resilience index system, and constructs an urban flood resilience evaluation index system based on the Pressure-State-Response (PSR) model and comprehensively considers the basic components of the city (society, economy, environment, infrastructure); adopts a combined weighting method to combine the subjective and objective factors to determine the final weight, wherein the entropy weight method determines the objective weight, and the hierarchical analysis method determines the subjective weight; considering the fuzziness and randomness of the dimensional division and indicator selection in the process of urban flood resilience evaluation, the cloud model is used to construct the evaluation model, and the normal cloud model is used to obtain the membership matrix and the corresponding weight value is combined to calculate the comprehensive determination degree and grade characteristic value of urban flood resilience, and finally determine the urban flood resilience evaluation grade. The present invention evaluates the level of urban flood resilience, explores the factors that affect urban flood resilience, provides a decision-making basis for optimizing urban flood resilience, and opens up new ideas for improving urban flood resilience from the perspective of resilience.

[0113] The urban flood resilience assessment method provided by the embodiment of the present invention has the following advantages compared with the existing assessment systems and methods:

[0114] In the process of urban flood resilience assessment, the construction of the indicator system ignores causal correlation and process dynamics, the weight calculation is too single, and there are uncertainties and ambiguities in the selection and dimension division of indicators.

[0115] The embodiment of the present invention considers the causal correlation and process dynamics of the index system, and combines the Pressure-State-Response (PSR) model with the basic components of the city (society, economy, environment, infrastructure) to construct an urban flood resilience assessment index system;

[0116] The embodiment of the present invention considers the scientific rationality of weight calculation and adopts a combined weighting method to combine subjective and objective factors to determine the final weight, wherein the entropy weight method determines the objective weight and the hierarchical analysis method determines the subjective weight.

[0117] The embodiment of the present invention takes into account the fuzziness and randomness of dimensional division and indicator selection in the process of urban flood resistance resilience assessment, introduces a cloud model to construct an assessment model, obtains the membership matrix from the normal cloud model and calculates the comprehensive certainty and grade characteristic values ​​of urban flood resistance resilience in combination with the corresponding weight values, and finally determines the urban flood resistance resilience assessment grade.

[0118] The embodiment of the present invention quantifies the urban flood resilience indicators during the evaluation process, which can clearly obtain the impact of each indicator on the urban flood resilience and identify the weak links in the resilience construction process, making it easier to formulate reasonable plans and take targeted measures to effectively improve the level of urban flood resilience.

Claims

1. A method for evaluating urban flood resilience, characterized in that: The following steps are involved: Based on the pressure-state-response model, the social, economic, environmental and infrastructure are combined as the criterion layer, and the corresponding specific changes in the social, economic, environmental and infrastructure are analyzed as the indicator layer to construct an urban flood resilience assessment indicator system table; According to the current characteristics of the city and the connotation of resilience, with reference to relevant literature and expert opinions, combined with field survey data, the grade standards of each indicator in the indicator layer are established, and the evaluation grades of the corresponding indicators in the indicator layer are divided; The importance of each indicator in the criterion layer and the indicator layer with evaluation levels is compared and assigned in pairs, the relative importance of each indicator to the upper level indicator is quantified, the judgment matrix is ​​constructed, the maximum characteristic root of the judgment matrix and its corresponding characteristic vector are solved, and the maximum characteristic root is tested for consistency according to the consistency verification formula and the average random consistency indicator value table. For the maximum characteristic root that fails the consistency, the corresponding judgment matrix is ​​readjusted until the verification is passed and the subjective weight is determined; The extreme value method is used to standardize each indicator of the indicator layer with evaluation levels, and the entropy weight method is used to calculate the weight vector of resilience evaluation indicators as the objective weight; According to the subjective weight and objective weight, the weight of resilience assessment index is determined by combining weights; According to each indicator in the indicator layer with evaluation levels, the expectation, entropy and super entropy in the cloud model are determined, by generating a first normal random number with a mean of entropy and a standard deviation of super entropy, generating a second normal random number with a mean of expectation and a standard deviation of entropy, and solving multiple memberships of the second normal random number to the qualitative concept; According to the weights and membership of resilience assessment indicators, the comprehensive membership of urban flood resilience is calculated, and the characteristic value of the assessment grade is calculated to determine the urban flood resilience grade.

2. A method for evaluating urban flood resilience according to claim 1, characterized in that: The construction judgment matrix adopts a nine-level scale of relative importance of factors.

3. A method for evaluating urban flood resilience according to claim 1, characterized in that: The method of solving the maximum eigenvalue of the judgment matrix and its corresponding eigenvector comprises the following steps: The product of each row of elements in the judgment matrix is ​​calculated, and the nth root of the product of each row of elements is calculated, the vector after the nth root is normalized, the maximum characteristic root is calculated based on the normalized data, and the characteristic vector is determined based on the maximum characteristic root.

4. A method for evaluating urban flood resilience according to claim 1, characterized in that: The consistency verification formula is as follows: Among them, RI is the average random consistency index, CR is the random consistency ratio, CI is the consistency index, and the consistency index CI formula is as follows: Among them, λ max is the maximum characteristic root of the judgment matrix, and n is the order in the average random consistency index value table.

5. A method for evaluating urban flood resilience according to claim 4, characterized in that: For the maximum characteristic root that fails the consistency, it is determined whether CR=CI / RI<0.

10. If so, it is manually determined that the consistency passes.

6. A method for evaluating urban flood resilience according to claim 1, characterized in that: A cloud graph is formed according to the multiple second normal random numbers and the multiple membership degrees, and the evaluation level of the indicator is displayed on the cloud graph.

Images