A method for evaluating urban flood resilience based on variable sets

Abstract

The present invention discloses a method for evaluating urban flood resilience based on a variable set, which belongs to the technical field of resilient city construction and flood disaster defense, and includes: determining the evaluation index of urban flood resilience based on the PSR model; using the entropy weight method to calculate the weight of the evaluation index; using the average division method to determine the flood disaster resilience evaluation grading standard of the evaluation index; using the variable set method to calculate the level characteristic value of the urban flood disaster resilience evaluation; and determining the urban flood resilience evaluation grade. The present invention constructs an evaluation method for urban water resilience with the help of PSR theory and variable set theory, and conducts research from the three resilience dimensions of pressure, state, and response. At the same time, it combines the principles of representativeness, scientificity, rationality, and availability of indicator selection, in order to discover the problems and weak links in urban resilience, and put forward practical and feasible suggestions for improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of resilient city construction and flood disaster prevention, and specifically relates to an urban flood resilience evaluation method based on a variable set. Background Art

[0002] With the acceleration of my country's urbanization process, the social underlying surface conditions are changing rapidly. The runoff and hydrological processes of the river basin have changed greatly compared with the past. The changes in urban hydrological mechanisms have also caused the frequent occurrence of waterlogging, mountain torrents and other phenomena. Other problems such as water shortage, serious water pollution, water ecological damage, and water safety damage are emerging in an endless stream. With the continuous construction of flood control projects such as reservoirs, sluices, embankments, and flood storage areas, the flood control engineering system in the river basin has been gradually improved, but the risks of flood disasters to the operation and development of the city are increasing day by day. In the context of the continued intensification of climate change and the continuous increase in natural disasters such as floods and waterlogging faced by cities in the future, the endless improvement of flood control and drainage capabilities has become unsustainable. Since then, the concepts of sponge cities and resilient cities have emerged and have been widely recognized and popularized at home and abroad. However, the current definitions of resilient cities vary. There are more than 70 existing definitions, most of which focus on the discussion of adaptability and resilience. Among them, in the field of flood disasters, the definition with higher recognition is: the ability of the system to resist, absorb, adapt and recover from flood disasters in a timely and effective manner. Therefore, improving urban resilience has important theoretical and practical significance for reducing the impact of flood disasters and promoting the sustainable development of cities in my country. Through the understanding and evaluation of the current situation, and on this basis, improving the urban flood resilience capacity is an inevitable process for implementing the concept of resilient cities. Therefore, it is very necessary to carry out the evaluation of resilient cities. Summary of the invention

[0003] The purpose of the present invention is to provide a method for evaluating urban flood resilience based on a variable set in view of the above-mentioned deficiencies in the prior art, so as to solve the problem that the prior art lacks evaluation of resilient cities.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A method for evaluating urban flood resilience based on a variable set comprises the following steps:

[0006] S1. Determine the evaluation index of urban flood resilience based on the PSR model;

[0007] S2, using the entropy weight method to calculate the weight of the evaluation index;

[0008] S3. Flood disaster resilience evaluation grading standard using the equal division method to determine the evaluation indicators;

[0009] S4. Based on the evaluation indicators and their weights, the variable set method is used to calculate the level characteristic values ​​of urban flood disaster resilience evaluation;

[0010] S5. Determine the urban flood resilience evaluation level based on the level characteristic values ​​and flood disaster resilience evaluation grading standards.

[0011] Furthermore, the evaluation indicators of urban flood resilience in S1 include: reservoir flood storage capacity / flood storage capacity of flood storage and detention areas, per capita road area, proportion of sponge city area in built-up areas, embankment compliance rate / non-hazardous reservoir rate, river network density, land development intensity, number of rainstorm centers, economic density, rainfall monitoring station density, proportion of villages in danger of mountain torrents / proportion of residential buildings within the flooding range, proportion of key infrastructure within the flooding range, and emergency plan system integrity index.

[0012] Furthermore, the S2 comprises the following sub-steps:

[0013] S21, standardize the evaluation indicators;

[0014] S22. Use the entropy weight method to calculate the weight of the evaluation index after standardization.

[0015] Furthermore, the S21 specifically includes:

[0016] Standardization of positive evaluation indicators:

[0017]

[0018] Standardization of negative evaluation indicators:

[0019]

[0020] Among them, X′ ij is the evaluation index value after standardization; X ij is the original evaluation index value; X maxj , X minj They are respectively j The maximum and minimum values ​​of the evaluation indicators.

[0021] Furthermore, the S22 specifically includes:

[0022] Calculate the j The evaluation index is i The proportion of groups :

[0023]

[0024] Calculate the j Information entropy of evaluation index :

[0025]

[0026] Calculate the j The utility value of the evaluation index :

[0027]

[0028] Calculate the j The weight of the evaluation index :

[0029]

[0030] in, m To count the number of groups; n is the number of indicators.

[0031] Furthermore, the S3 specifically includes:

[0032] The evaluation indicators are divided into five levels, namely level 1-poor, level 2-average, level 3-medium, level 4-good, and level 5-excellent.

[0033] Furthermore, the S4 comprises the following sub-steps:

[0034] S41. Construct an index standard value interval matrix based on the five levels of evaluation indicators;

[0035] S42, constructing a point value mapping matrix with a relative difference of 1 according to the index standard value interval matrix;

[0036] S43, based on the gradually changing qualitative change point values ​​in the index standard value interval matrix, construct a point value mapping matrix with relative difference degrees of 1 and 0;

[0037] S44. Construct a relative membership matrix based on the point value mapping matrix, and use the variable set method to calculate the level eigenvalues ​​of urban flood disaster resilience evaluation.

[0038] Furthermore, in S41 Indicators The index standard value interval matrix I for each level is:

[0039]

[0040] in, a is the upper limit of the standard value interval, b is the lower limit of the standard value interval; h is the current level of the evaluation indicator; For evaluation indicators i exist h The upper bound of the level interval, For evaluation indicatorsi exist h The lower bound of the level interval.

[0041] Furthermore, the point value mapping matrix with a relative difference of 1 in S42 M for:

[0042]

[0043]

[0044] in, For indicators In the interval Internal pair The point value with a relative membership of 1.

[0045] Further, the S44 includes the following sub-steps:

[0046] S441. Resilient cities to be evaluated Evaluation indicators The eigenvalue of Falling into the matrix M Between two adjacent levels The resilient city is the object of evaluation. right Level evaluation index The relative membership of is:

[0047]

[0048] Or:

[0049]

[0050] S442, for eigenvalues Not in the range , , which is less than Level, greater than Level evaluation index The relative membership of should be 0:

[0051]

[0052] For other eigenvalues, we have:

[0053]

[0054] S443. Based on S441 and S442, obtain the resilient city to be evaluated Evaluation indicators The eigenvalue of For all levels hThe relative membership matrix of the indicators is:

[0055]

[0056] S444. Calculate the evaluation index weight vector :

[0057]

[0058] in, is the weight of the mth evaluation index;

[0059] S445. According to the evaluation index weight vector , calculate the resilient city to be evaluated Level h The comprehensive relative membership can be expressed as vector for:

[0060]

[0061] S446, according to the comprehensive relative membership, the vector formula can be used , calculate the application level feature value:

[0062]

[0063] in, is the level characteristic value.

[0064] The urban flood resilience evaluation method based on variable sets provided by the present invention has the following beneficial effects:

[0065] 1. This paper constructs an evaluation method for urban water resilience with the help of PSR theory, and conducts research from three resilience dimensions: pressure, state, and response based on the PSR model. At the same time, it combines the principles of representativeness, scientificity, rationality, and availability of indicator selection, in order to discover the problems and weak links in urban resilience, and put forward practical suggestions for improvement.

[0066] 2. The evaluation index system proposed in this invention comprehensively considers the comprehensive capabilities of the engineering projects such as "infiltration, stagnation, storage, and drainage" that determine urban flooding, and quantitatively designs the non-engineering measures indicators based on practical experience and research, reflecting the system coupling effect of engineering background and non-engineering measures. However, the currently commonly used resilience evaluation methods can only reflect part of the engineering resilience or non-engineering resilience, and cannot fully reflect it. From the results, the evaluation results based on this method are consistent with the actual status of urban development and meet the practical requirements.

[0067] 3. The variable set theory is a further development based on the original variable fuzzy set. Since its proposal, the theory has been fully recognized in the evaluation field. Resilient cities are a product formed and continuously improved in the complex interaction process between humans and natural society. By sorting out resilience indicators, it is found that they have strong applicability to the variable set theory. However, there are no relevant results on flood resilience evaluation based on this theory. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of the urban flood resilience evaluation method based on variable sets. DETAILED DESCRIPTION

[0069] The specific implementation modes of the present invention are described below to facilitate those skilled in the art to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0070] Example 1

[0071] This embodiment provides a method for evaluating urban flood resilience based on variable sets. This embodiment uses the PSR model combined with variable set theory to scientifically and reasonably evaluate urban flood resilience, in order to discover the problems and weak links of urban resilience and put forward feasible improvement suggestions. Figure 1 , which specifically includes the following:

[0072] Step S1, determining the evaluation index of urban flood resilience based on the PSR model;

[0073] The PSR model of this embodiment is a pressure-state-response model, which is an environmental assessment model jointly proposed by the Organization for Economic Cooperation and Development (OECD) and the United Nations Environment Program (UNEP). The PSR model believes that human beings obtain resources from nature, produce and consume them, and generate certain wastes, so human activities have an impact on the ecological environment; changes in the ecological environment react on humans, so humans have taken certain social, ecological, and natural measures to alleviate and restore to the original state. The cyclical changes in this process constitute the pressure-state-response relationship between humans and society, resources, and ecology.

[0074] The evaluation indicators include: reservoir flood storage capacity / flood storage capacity of flood storage areas, per capita road area, proportion of sponge city area in built-up areas, embankment compliance rate / rate of non-dangerous reservoirs, river network density, land development intensity, number of rainstorm centers, economic density, density of rainfall monitoring stations, proportion of villages in danger of flash floods / proportion of residences within the flooding range, proportion of key infrastructure within the flooding range, and emergency plan system integrity index.

[0075] Step S2: Calculate the weight of the evaluation index using the entropy weight method, which specifically includes the following sub-steps:

[0076] Step S21, in order to eliminate the calculation errors caused by different dimensions of the original data of each indicator, the original data is standardized by using the range standardization method;

[0077] Standardization of positive evaluation indicators:

[0078]

[0079] Standardization of negative evaluation indicators:

[0080]

[0081] Among them, X′ ij is the evaluation index value after standardization; X ij is the original evaluation index value; X maxj , X minj They are respectively j The maximum and minimum values ​​of the evaluation indicators.

[0082] Step S22: Calculate the weight of the standardized evaluation index using the entropy weight method:

[0083] Calculate the j The evaluation index is i The proportion of groups :

[0084]

[0085] Calculate the j Information entropy of evaluation index :

[0086]

[0087] Calculate the j The utility value of the evaluation index :

[0088]

[0089] Calculate the j The weight of the evaluation index :

[0090]

[0091] in, m To count the number of groups; n is the number of indicators.

[0092] Step S3, using the equal division method to determine the flood disaster resilience evaluation grading standard of the evaluation index;

[0093] According to the actual situation of each evaluation indicator, determine the flood disaster resilience evaluation classification standard for each evaluation indicator;

[0094] Due to the large differences in evaluation objects, it has been difficult to have a relatively unified evaluation standard. This invention combines different types of data and industry regulations and divides each evaluation index into 5 levels based on actual conditions: the toughness levels are 1 (poor), 2 (general), 3 (medium), 4 (good), and 5 (excellent).

[0095] In this embodiment, since the flood storage capacity is evaluated in two spatial ranges, mountainous area and urban area, and the values ​​of reservoirs and flood storage areas are quite different, this evaluation index is not graded using the equal division method, but is graded based on actual conditions.

[0096] Step S4: Based on the evaluation index and the weight of the evaluation index, the variable set method is used to calculate the level characteristic value of the urban flood disaster resilience evaluation, which specifically includes the following sub-steps:

[0097] Step S41: construct an index standard value interval matrix according to the five levels of the evaluation index, which specifically includes the following contents:

[0098] The set of objects to be evaluated for resilient cities is as follows:

[0099]

[0100] Known object set The indicator eigenvalue matrix is:

[0101]

[0102] Indicators The index standard value interval matrix I for each level is:

[0103]

[0104] in, a is the upper limit of the standard value interval, b is the lower limit of the standard value interval; his the current level of the evaluation indicator; For evaluation indicators i exist h The upper bound of the level interval, For evaluation indicators i exist h The lower bound of the level interval.

[0105] Step S42: construct a point value mapping matrix with a relative difference of 1 according to the index standard value interval matrix I, which specifically includes the following contents:

[0106] Level 1 It is a low-risk level. According to the physical concept, the evaluation index Level 1 standard value interval The upper bound of The relative membership to level 1 is 1. According to the law of unity of opposites, the relative membership to the opposite level 2 is 0. The relative membership to level 1 is 0.5, and the relative membership to the opposite level 2 is 0.5. According to the mass conversion theorem, is the gradual qualitative change point between level 1 and level 2. For indicators In the interval The point value of the relative membership degree of the inner pair 1 is 1, so = .

[0107] make Level It is a special risk level. According to the physical concept, the interval The lower bound right The relative membership of the level is 1, and the relative membership of the opposite level is The relative membership degree is 0; the upper bound right The relative membership of the level is 0.5, and the relative membership of the opposite level is 0.5. The relative membership of is 0.5, that is, for The gradual qualitative change point between level 1 and level c; For indicators In the interval Internal pair The point value of the relative membership of the level is 1, = .

[0108] make Level For intermediary insurance level. If it is an odd number, the intermediary risk level It is an intermediate level with neither good nor bad performance. Its evaluation index is Standard value range The upper and lower bounds of , right The relative membership of each level is 0.5. and The relationship between the levels has intervals: and coincide, and Overlap, the former The relative membership of the level is 0.5, which is The relative membership of the level is 0.5. Intermediate level interval The point value with a relative membership of 1, according to the physical concept, The point value should be The midpoint of the level interval, .like is an even number, there is no intermediate level.

[0109] In summary, we can get the point value mapping matrix with a relative difference of 1 M for:

[0110]

[0111]

[0112] in, For indicators In the interval Internal pair The point value of the relative membership of the level is 1

[0113] Step S43: construct a point value mapping matrix with relative differences of 1 and 0 based on the gradually changing qualitative change point values ​​in the index standard value interval matrix.

[0114] Step S44, constructing a relative membership matrix based on the point value mapping matrix, and using a variable set method to calculate the level eigenvalue H of the urban flood disaster resilience evaluation, which specifically includes the following sub-steps:

[0115] Step S441: Know the object to be evaluated according to the indicator eigenvalue matrix of m The indicator eigenvalue vector is:

[0116]

[0117] Resilient cities to be evaluated Evaluation indicators The eigenvalue of Falling into the matrix M Between two adjacent levels The resilient city is the object of evaluation. right Level evaluation index The relative membership of is:

[0118]

[0119] Or:

[0120]

[0121] in, , it can be seen from the above two equations that when hour, , according to the unity of opposites theorem, ;when hour, , by the unity of opposites theorem ;when hour, ,have .

[0122] Step S442: According to the physical concept, for the characteristic value Not in the range , , which is less than Level, greater than Level evaluation index The relative membership of should be 0, then:

[0123]

[0124] For the larger or smaller the better index, according to the physical concept, we have:

[0125]

[0126] Step S443: Obtain the resilient city to be evaluated based on step S441 and step S442 Evaluation indicators The eigenvalue of For all levels h The relative membership matrix of the indicators for:

[0127]

[0128] In this embodiment, for simplicity, the matrix That is .

[0129] Step S444: According to the evaluation index i The importance of is different, calculate the evaluation index weight vector :

[0130]

[0131] in, is the weight of the mth evaluation index;

[0132] Step S445: According to the evaluation index weight vector , calculate the resilient city to be evaluated Level h The comprehensive relative membership can be expressed as vector for:

[0133]

[0134] Step S446: According to the comprehensive relative membership, the available vector formula , calculate the application level feature value:

[0135]

[0136] in, is the level characteristic value

[0137] Step S5: Determine the urban flood resilience evaluation level according to the level characteristic value and the flood disaster resilience evaluation grading standard.

[0138] Although the specific implementation of the invention is described in detail in conjunction with the drawings, it should not be understood as limiting the scope of protection of this patent. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still fall within the scope of protection of this patent.

Claims

1. A method for evaluating urban flood resilience based on variable sets, characterized in that: The following steps are involved: S1. Determine the evaluation index of urban flood resilience based on the PSR model; S2, using the entropy weight method to calculate the weight of the evaluation index; S3. Flood disaster resilience evaluation grading standard using the equal division method to determine the evaluation indicators; S4. Based on the evaluation indicators and their weights, the variable set method is used to calculate the level characteristic values ​​of urban flood disaster resilience evaluation; S5. Determine the urban flood resilience evaluation level based on the level characteristic value and flood disaster resilience evaluation grading standards; The S4 comprises the following sub-steps: S41. Construct an index standard value interval matrix based on the five levels of evaluation indicators; S42, constructing a point value mapping matrix with a relative difference of 1 according to the index standard value interval matrix; S43, based on the gradually changing qualitative change point values ​​in the index standard value interval matrix, construct a point value mapping matrix with relative difference degrees of 1 and 0; S44. Construct a relative membership matrix based on the point value mapping matrix, and use the variable set method to calculate the level eigenvalues ​​of urban flood disaster resilience evaluation; In S41, the set of objects to be evaluated U of resilient cities is as follows: U={u1,u2,u3…u n }={u j },j=1,2,…,n The m indicator eigenvalue matrix X of the known object set is: The index standard value interval matrix I for m indicators and c levels is: Among them, a is the upper limit of the standard value interval, b is the lower limit of the standard value interval; h is the current level of the evaluation index; a ih is the upper bound of the evaluation index i in the h-level interval, b ih is the lower bound of the evaluation index i in the h-level interval; The point value mapping matrix M with a relative difference of 1 in S42 is: Among them, M ih For index i in the interval [a ih ,b ih ] The point value with a relative membership of level c is 1; The S44 comprises the following sub-steps: S441, according to the indicator eigenvalue matrix, the object u to be evaluated is known j The m indicator eigenvalue vectors for: Resilient city to be evaluated j The characteristic value x of the evaluation index i ij Falling into two adjacent intervals in the matrix M [M ih ,M i(h+1) ], then the resilient city to be evaluated object u j The relative membership degree of the h-level evaluation index i is: Or: S442, for the eigenvalue x ij Not falling into the interval [M i(h-1) ,M ih ]、[M i(h+1) ,M i(h+2) ], that is, the relative membership of the evaluation index i that is less than level h and greater than level (h+1) should be 0: For other eigenvalues, we have: S443. Based on S441 and S442, we get the resilient city object u to be evaluated. j The characteristic value x of the evaluation index i ij The relative membership matrix of indicators at each level h is: Among them, the matrix μ(u j ) is μ(x ij ); S444. Calculate the evaluation index weight vector Among them, w m is the weight of the mth evaluation index; S445. According to the evaluation index weight vector Calculate the resilient city object u to be evaluated j The comprehensive relative membership of level h can be expressed as vector for: S446, according to the comprehensive relative membership, the vector formula can be used Calculate application-level feature values: Among them, H(u j ) is the level characteristic value.

2. The urban flood resilience evaluation method based on variable sets according to claim 1 is characterized in that: The evaluation indicators of urban flood resilience in S1 include: reservoir flood storage capacity / flood storage capacity of flood storage and detention areas, per capita road area, proportion of sponge city area in built-up areas, embankment compliance rate / non-hazardous reservoir rate, river network density, land development intensity, number of rainstorm centers, economic density, rainfall monitoring station density, proportion of villages in danger of flash floods / proportion of residential buildings within the flooding range, proportion of key infrastructure within the flooding range, and emergency plan system integrity index.

3. The urban flood resilience evaluation method based on variable sets according to claim 1 is characterized in that: The S2 comprises the following sub-steps: S21, standardize the evaluation indicators; S22. Use the entropy weight method to calculate the weight of the evaluation index after standardization.

4. The urban flood resilience evaluation method based on variable sets according to claim 3 is characterized in that: The S21 specifically includes: Standardization of positive evaluation indicators: Standardization of negative evaluation indicators: Among them, X′ ij is the evaluation index value after standardization; X ij is the original evaluation index value; X maxj , X minj are the maximum and minimum values ​​of the j-th evaluation index in the group, respectively.

5. The urban flood resilience evaluation method based on variable sets according to claim 4 is characterized in that: The S22 specifically includes: Calculate the weight P of the jth evaluation index in the i-th group ij : Calculate the information entropy E of the jth evaluation index j : Calculate the utility value D of the jth evaluation index j : D j =1-E j Calculate the weight W of the jth evaluation index j : Among them, m is the number of statistical groups; n is the number of indicators.

6. The urban flood resilience evaluation method based on variable sets according to claim 1 is characterized in that: The S3 specifically includes: The evaluation indicators are divided into five levels, namely level 1-poor, level 2-average, level 3-medium, level 4-good, and level 5-excellent.

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