Urban flood toughness evaluation method based on variable set

Abstract

The invention discloses an urban flood toughness evaluation method based on a variable set, and belongs to the technical field of tough city construction and flood disaster defense, and the method comprises the steps: determining an evaluation index of the urban flood toughness capability based on a PSR model; calculating the weight of the evaluation index by adopting an entropy weight method; determining a flood disaster toughness evaluation grading standard of the evaluation indexes by adopting an equipartition method; calculating a level characteristic value of urban flood disaster toughness evaluation by adopting a variable set method; and determining an urban flood toughness evaluation grade. The evaluation method for urban water toughness is constructed by means of the PSR theory and the variable set theory, research is carried out from three toughness dimensions of pressure, state and response, and meanwhile, the principles of representativeness, scientificity, rationality, availability and the like of index selection are combined, so that problems and weak links existing in urban toughness are expected to be found, and the urban water toughness is evaluated. And feasible promotion suggestions are provided.

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 continuous construction of flood control projects such as reservoirs, sluices, levees, and flood storage areas, the flood control engineering system in the basin is becoming more and more complete, but the risks of flood disasters to the operation and development of cities are increasing day by day. With the continuous intensification of climate change and the increasing number of natural disasters such as floods faced by cities in the future, it has become unsustainable to endlessly improve the flood control and drainage capacity. 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 are not the same. There are more than 70 definitions of existing technologies, most of which are discussed around 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 are the maximum and minimum values ​​of the j-th evaluation index in the group, respectively.

[0021] Furthermore, the S22 specifically includes:

[0022] Calculate the weight P of the jth evaluation index in the i-th group ij :

[0023]

[0024] Calculate the information entropy E of the jth evaluation index j :

[0025]

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

[0027] D j =1-E j

[0028] Calculate the weight W of the jth evaluation index j :

[0029]

[0030] Among them, m is the number of statistical 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, the index standard value interval matrix I of the m indexes and c levels in S41 is:

[0039]

[0040] 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 It is the lower bound of the evaluation index i in the h level interval.

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

[0042]

[0043] Among them, M ih For index i in the interval [a ih ,b ih ]The point value with relative membership of level c is 1.

[0044] Further, the S44 includes the following sub-steps:

[0045] S441, Resilient city evaluation object u 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:

[0046]

[0047] Or:

[0048]

[0049] 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:

[0050]

[0051] For other eigenvalues, we have:

[0052]

[0053] 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:

[0054]

[0055] S444. Calculate the evaluation index weight vector

[0056]

[0057] Among them, w m is the weight of the mth evaluation index;

[0058] 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:

[0059]

[0060] S446, according to the comprehensive relative membership, the vector formula can be used Calculate application-level feature values:

[0061]

[0062] Among them, H(u j ) is the level characteristic value.

[0063] The urban flood resilience evaluation method based on variable sets provided by the present invention has the following beneficial effects:

[0064] 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.

[0065] 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.

[0066] 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

[0067] Figure 1 This is a flow chart of the urban flood resilience evaluation method based on variable sets.

[0068] Figure 2 It is the resilience level of each group in Beijing. DETAILED DESCRIPTION

[0069] The specific implementation modes of the present invention are described below so that those skilled in the art can 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 ijis 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.

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

[0083] Calculate the weight P of the jth evaluation index in the i-th group ij :

[0084]

[0085] Calculate the information entropy E of the jth evaluation index j :

[0086]

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

[0088] D j =1-E j

[0089] Calculate the weight W of the jth evaluation index j :

[0090]

[0091] Among them, m is the number of statistical 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. The present invention combines different types of data and industry regulations and divides the evaluation indicators into 5 levels based on actual conditions: the toughness levels are level 1 (poor), level 2 (general), level 3 (medium), level 4 (good), and level 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] U={u1,u2,u3…u n}={u j},j=1,2,…,n

[0100] The m indicator eigenvalue matrix of the known object set is:

[0101]

[0102] The index standard value interval matrix I for m indicators and c levels is:

[0103]

[0104] 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 It is the lower bound of the evaluation index i in the h 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] Let level 1, i.e. h = 1, be the low risk level. From the physical concept, we know that the level 1 standard value interval of the evaluation index i is [a i1 ,b i1 ] i1 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 lower bound b i1 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, b i1 is the gradual qualitative change point between level 1 and level 2. Let M i1 For index i in the interval [a i1 ,b i1 ] has a relative membership of 1 to level 1, so M i1 =a i1 .

[0107] Let c level, that is, h = c, be the special risk level. From the physical concept, we know that the interval [a ic ,b ic ] is the lower bound b ic The relative membership to level c is 1, and the relative membership to the opposite level (c-1) is 0; the upper bound aic The relative membership to level c is 0.5, and the relative membership to the opposite level (c-1) is 0.5, that is, a ic is the gradual qualitative change point between (c-1) level and c level; let M ic For index i in the interval [a ic ,b ic ] For the point value of level c with a relative membership of 1, there is M ic =b ic .

[0108] Let level l, that is, h = l, be the intermediary insurance level. If c is an odd number, then the intermediary insurance level l = (c + 1) / 2 is a medium-level intermediary level, and its evaluation index i standard value interval [a il ,b il ]'s upper and lower bounds a il 、b il The relative membership of level l is 0.5. However, their relationship with the adjacent levels (l-1) and (l+1) is in the range: a il With b i(l-1) Overlap, b il with a i(l+1) The former has a relative membership of 0.5 to the (l-1) level, and the latter has a relative membership of 0.5 to the (l+1) level. il For the intermediate level interval [a il ,b il ] The relative membership is 1. According to the physical concept, M il The point value should be the midpoint of the l-level interval, that is, M il =(a il +b il ) / 2. If c is an even number, there is no intermediate level.

[0109] In summary, the point value mapping matrix M with a relative difference of 1 is:

[0110]

[0111] Among them, M ih For index i in the interval [a ih ,b ih ] The point value of the relative membership degree of level c is 1

[0112] 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.

[0113] 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:

[0114] Step S441: Know the object u to be evaluated according to the indicator eigenvalue matrix j The m indicator eigenvalue vectors are:

[0115]

[0116] 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:

[0117]

[0118] Or:

[0119]

[0120] Among them, h=1,2,…,(c-1), from the above two equations, it can be seen that when x ij =M ih When ih (x ij )=1, according to the law of unity of opposites, μ i(h+1) (x ij )=0; when x ij =b ih When ih (x ij )=0.5, according to the law of unity of opposites μ i(h+1) (x ij )=0.5; when x ij =M i(h+1) When ih (x ij )=0, there is μ i(h+1) (x ij )=1.

[0121] Step S442: According to the physical concept, 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, then:

[0122]

[0123] For the larger or smaller the better index, according to the physical concept, we have:

[0124]

[0125] Step S443: Based on step S441 and step S442, obtain the resilient city to be evaluated object u j The characteristic value x of the evaluation index i ij For each level h, the relative membership matrix μ(u j )for:

[0126]

[0127] In this embodiment, for simplicity, the matrix μ(u j ) is μ(x ij ).

[0128] Step S444: Calculate the evaluation index weight vector according to the importance of the evaluation index i

[0129]

[0130] Among them, w m is the weight of the mth evaluation index;

[0131] Step 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:

[0132]

[0133] Step S446: According to the comprehensive relative membership, the available vector formula Calculate application-level feature values:

[0134]

[0135] Among them, H(u j ) is the level characteristic value

[0136] Step S5: Determine the urban flood resilience evaluation level according to the level characteristic value and the flood disaster resilience evaluation grading standard.

[0137] Example 2

[0138] This example uses the method in Example 1 as a basis to evaluate the urban flood resilience in Beijing, which specifically includes the following contents:

[0139] Step T1, determine the evaluation indicators of urban water resilience in Beijing;

[0140] In the PSR model of this embodiment, P stands for pressure. The pressure index represents the impact of human economic and social activities and natural activities on the environment, which is usually a negative effect. S stands for state. The state index represents the environmental state and environmental changes in a specific time period, and is an objective indicator that is constantly changing. R stands for response. The response index means that in the face of existing impacts, relevant departments take corresponding measures to improve and restore. Based on the PSR model, research is conducted from the three resilience dimensions of pressure, state, and response. At the same time, the representativeness, scientificity, rationality, and availability of indicator selection principles, the research of relevant scholars, and the actual development status of Beijing should be combined to construct an urban flood disaster resilience index system in Beijing, in order to discover the problems and weak links in urban resilience and put forward practical suggestions for improvement.

[0141] The 12 evaluation indicators selected are:

[0142] Reservoir flood storage capacity / flood storage area flood storage capacity (m 3 / km 2 ), per capita road area (10,000 m 2 / 10,000 people), proportion of built-up area of ​​sponge cities (%), levee compliance rate / non-hazardous reservoir rate (%), river network density (km / km 2 ), land development intensity (%), number of rainstorm centers (times / year), economic density (10,000 yuan / km 2 ), density of rainfall monitoring stations (number / km 2 ), proportion of villages at risk of flash floods / proportion of residential buildings within the flooding area (%), proportion of key infrastructure within the flooding area (%), and emergency plan system integrity index (dimensionless).

[0143] Based on the principle of selecting evaluation indicators, some indicators (3) of the 12 evaluation indicators of flood disaster resilience in Beijing are divided into two spatial ranges, mountainous area and urban area, for evaluation in order to better reflect the characteristics of Beijing and make the analysis more accurate, as shown in Table 1:

[0144] Table 1 Summary of urban flood disaster resilience indicators

[0145]

[0146] An evaluation indicator with a positive nature is a benefit-type indicator, which means that the larger the indicator value, the better the evaluation effect; an evaluation indicator with a negative nature is a cost-type indicator, which means that the smaller the indicator value, the better the evaluation effect.

[0147] The meanings of each evaluation indicator are as follows:

[0148] 1. Flood storage capacity of reservoirs / flood storage capacity of flood storage areas;

[0149] The evaluation is conducted based on the flood storage capacity of the reservoirs / flood storage areas within the evaluation area, and the parameters related to the reservoirs / flood storage areas are obtained from the plan.

[0150] 2. Per capita road area

[0151] The per capita road area index measures the breadth of the road network and its ability to serve the population, and is an important aspect of evaluating evacuation and emergency rescue during disaster emergency response. The larger the per capita road area, the more evacuation routes residents have and the higher the rescue efficiency in emergency situations such as floods. Road data includes urban and rural road land, rural roads, and highway land, which are extracted from land use data.

[0152] 3. Proportion of sponge city area in built-up areas

[0153] The evaluation is conducted based on the proportion of the built-up area that meets the requirements of the Sponge City Construction Evaluation Standard (GB / T 51345) to the total built-up area within the evaluation area.

[0154]

[0155] 4. Dike compliance rate / non-hazardous reservoir rate

[0156] This indicator reflects the compliance rate of flood control projects, the compliance rate of levees (plains) and the rate of non-hazardous reservoirs (mountainous areas), which are key indicators for assessing the status of regional flood control infrastructure. The levee compliance data comes from the first national comprehensive natural disaster risk survey in Beijing, and the data on hazardous reservoirs comes from the Reservoir Data Manual (2022).

[0157] 5. River network density

[0158] River network density is a geographical and hydrological indicator that measures the development and density of surface rivers and water systems. It is defined as the total length of rivers in a region divided by the total area of ​​the region. A high river network density may indicate that the basin has a faster runoff response and a higher frequency of water exchange, which is crucial for the basin's buffering and recovery capabilities in the face of extreme precipitation events. The water system data comes from the first national water conservancy census.

[0159] 6. Land development intensity

[0160] Land development intensity describes the extent of construction and hardened ground in urban areas, which directly affects the natural permeability of the ground, and thus affects the discharge and accumulation of flood water. Higher development intensity is usually associated with low permeability of the underlying surface, increasing the risk of flooding. This indicator is quantified by analyzing the impervious area in land use type data. The impervious area statistics include commercial service facility land, industrial and mining storage land, residential land, public administration and public service land, transportation land, etc. The impervious area in the plain area is much higher than that in the mountainous area, and the impervious area in the central urban area is higher than that in the suburbs. This indicator is a negative indicator.

[0161] Land development intensity = impervious area / regional area × 100%

[0162] 7. Number of rainstorm centers

[0163] The number of rainstorm centers reflects the frequency of rainstorm events in history. The rainstorm center refers to the point with the largest rainfall in a rainstorm event and is a key factor in assessing the risk of rainstorms and floods faced by a region. The rainstorm center is the weighted center of rainfall in historical rainstorm events. This indicator is a negative indicator.

[0164] 8. Economic density

[0165] Economic density reflects the concentration of economic activities and per capita economic output, and embodies the economic strength of a region and a population. Economically developed regions perform better in terms of resistance and recovery capabilities to flood disasters. The GDP data comes from the first national comprehensive natural disaster risk survey in Beijing.

[0166] 9. Rainfall monitoring station density

[0167] The density of rainfall monitoring stations reflects a region's ability to monitor meteorological conditions, which is crucial for issuing timely flood warnings. A dense monitoring network can provide more accurate data to help predict and respond to flood events. This indicator is determined by counting the number of rainfall stations in a specific area, including meteorological rainfall stations and urban and rural rainfall stations (built by the Water Affairs Bureau).

[0168] 10. Proportion of villages at risk of flash floods / proportion of residential buildings within the flooding area

[0169] The proportion of villages at risk of flash floods refers to the proportion of villages along rivers in mountainous areas, reflecting the risk of flood impact in mountainous villages. The proportion of residential buildings within the flooded area reflects the proportion of the number of residential buildings in the flooded area based on the flood risk scenario to the total number of residential buildings in the area. This indicator is mainly applicable to plain areas. A high value indicates that a large proportion of residents are directly facing flood risks. Villages at risk of flash floods are extracted from remote sensing impact data, and residential buildings are derived from land use data. This indicator is a negative indicator.

[0170] 11. Proportion of critical infrastructure within the flooded area

[0171] The indicator reflects the proportion of key infrastructure in the inundated area based on flood risk scenarios to the total infrastructure area. The key infrastructure data uses public service land, including government agencies, science, education, culture, health and public facilities, etc. The data comes from land use. This indicator is a negative indicator.

[0172] 12. Plan Completeness Index

[0173] In order to evaluate the flood defense resilience of each district, a "Plan Integrity Index" was established from the perspective of flood control plans. The index was evaluated based on categories such as flood control plan coverage level, resource (materials, teams, communications, information technology) allocation, emergency drills, evaluation and update mechanisms, and personnel evacuation.

[0174] Table 2: Scoring criteria for emergency plans

[0175]

[0176]

[0177] In order to more accurately evaluate the resilience of the 16 districts in Beijing, this example divides them into 39 groups, and lists the 12 index values ​​of the 39 groups in sequence. F1 represents the flood storage capacity of the reservoir / flood storage capacity of the flood storage area (m 3 / km 2 ), F2 represents the per capita road area (10,000 m 2 / 10,000 people), F3 represents the proportion of sponge city area in built-up areas (%), F4 represents the levee compliance rate / non-hazardous reservoir rate (%), F5 represents the river network density (km / km 2 ), F6 represents land development intensity (%), F7 represents the number of rainstorm centers (times / year), F8 represents economic density (10,000 yuan / km 2 ), F9 represents the density of rainfall monitoring stations (number / km 2 ), F10 represents the proportion of villages at risk of flash floods / the proportion of residential buildings within the flooding range (%), F11 represents the proportion of key infrastructure within the flooding range (%), and F12 represents the integrity index of the emergency plan system (dimensionless), as shown in Table 3:

[0178] Table 3 Index values ​​of 39 groups

[0179]

[0180]

[0181] Step T2, the entropy weight method is used to calculate the weight of the evaluation index, as shown in Table 4:

[0182] Table 4 Weight of each indicator

[0183]

[0184] Step T3, using the average division method to determine the flood disaster resilience evaluation grading standard of the evaluation index; Step T4, using the variable set method to calculate the level characteristic value of the urban flood disaster resilience evaluation; First, construct the indicator standard value interval matrix I of the 5-level evaluation index:

[0185]

[0186]

[0187] Construct a point value mapping matrix M with a relative difference of 1:

[0188]

[0189] Use the gradual qualitative change point value b in matrix I ih Get the relative difference 1, 0 point value mapping matrix T:

[0190]

[0191]

[0192] Calculate the relative membership matrix μ(u j ), and then calculate μ(u j )'s level eigenvalue H(u j ), as shown in Table 5:

[0193] Table 5 Characteristic values ​​of each group level

[0194]

[0195] T5. Based on the calculated level characteristic values, the resilience evaluation level of each group in Beijing is obtained.

[0196] refer to Figure 2Among the 39 resilience clusters in Beijing, 21 are of general grade, 13 are of medium grade and 5 are of good grade, indicating that the overall flood resilience of Beijing is generally not high. Due to the particularity of flood disasters, the flood resilience of the clusters with large and medium-sized reservoirs in western Yanqing, northern Miyun, southern Miyun, western Mentougou and southern Pinggu is obviously preferred, while the resilience of the clusters with fewer water interception and storage projects such as reservoirs and flood storage areas is relatively poor. At the same time, considering social factors such as regional economy and population density, the plain areas such as southern Haidian, Xicheng, central Chaoyang and Yizhuang have strong economic capacity and relatively good resilience, while the mountainous and shallow mountainous areas in eastern Fangshan, western Fangshan, northern Huairou, southern Huairou, eastern Yanqing, eastern Changping and northern Changping have small economic density, but relatively vast and sparsely populated, and their resilience is also at a medium level.

[0197] Judging from the evaluation results, they are generally consistent with the actual situation, which shows that the resilient city evaluation method based on variable set theory of the present invention is reasonable and the results are true and credible.

[0198] 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 values ​​and flood disaster resilience evaluation grading standards.

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.

7. The urban flood resilience evaluation method based on variable sets according to claim 1 is characterized in that: 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.

8. The urban flood resilience evaluation method based on variable sets according to claim 7 is characterized in that: The index standard value interval matrix I of m indexes and c levels in S41 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 It is the lower bound of the evaluation index i in the h level interval.

9. The urban flood resilience evaluation method based on variable sets according to claim 7 is characterized in that: 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 relative membership of level c is 1.

10. The urban flood resilience evaluation method based on variable sets according to claim 8 is characterized in that: The S44 comprises the following sub-steps: S441, Resilient city to be evaluated objectu 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: 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.

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