A coupling coordination degree analysis method for water resource-water environment-water ecology-water disaster coupling system

Abstract

The application provides a coupling coordination degree analysis method of a water resource-water environment-water ecology-water disaster correlation system, and the method comprises the following steps: collecting and screening key factors of the water resource-water environment-water ecology-water disaster correlation system, analyzing the space-time variation characteristics of the key factors, establishing an index evaluation system of each correlation system, determining the weight of the index system of each correlation system, and analyzing the coupling coordination degree of the water resource-water environment-water ecology-water disaster correlation system. The method realizes quantitative decoupling of the coupling coordination degree between the four systems of water resource, water environment, water ecology and water disaster, and is beneficial to improving the comprehensiveness of water resource evaluation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water resources management, and particularly relates to a coupling coordination degree analysis method of a water resource-water environment-water ecology-water disaster correlation system. BACKGROUND

[0002] Water resources management is an important part of realizing sustainable development of water resources. For the study of coupling effects, the commonly used methods include environmental Kuznets curve, double exponential model, nonlinear dynamics model, coupling degree model, grey correlation degree analysis, dynamic coupling model, vector autoregression model, spatial regression model, GIS gravity curve optimization analysis, etc. The coupling coordination degree model uses coupling degree to explain the mutual relationship between several subsystems, and further uses coupling development degree to comprehensively evaluate and study the whole system. Due to the simplicity and intuitive results of the model, it is widely used in the fields of water conservancy, energy, food, economy, population, urbanization, etc.

[0003] At present, the coupling coordination model related to water mostly couples water as a whole with non-water systems, and cannot consider different systems such as water resources, water environment, water ecology and water disaster, so it cannot comprehensively understand the coupling coordination of the internal water system. In terms of index selection, the existing method considers the index not comprehensively and subjectively, resulting in that the selected index is not representative and cannot reflect the characteristics of water. In terms of weight calculation, the subjective and objective combination weighting method is commonly used, and the weight result is neither subjective nor objective, but the weight results of the two methods are prone to conflict, and increasing the weight calculation method can increase the available quantity of the method result, thereby improving the fault tolerance of the weight result.

[0004] Therefore, how to more scientifically and comprehensively evaluate the water system is a problem to be solved at present. SUMMARY

[0005] In view of the problems in the prior art, the purpose of the present application is to provide a coupling coordination degree analysis method of a water resource-water environment-water ecology-water disaster correlation system, which selects key factors by using a subjective and objective combination method, selects optimal weights by using a combination weighting method based on game theory and a plurality of weight calculation methods, and proposes a coupling coordination degree analysis method of a water resource-water environment-water ecology-water disaster correlation system, which quantitatively decouples the coupling coordination degrees between four systems of water resources, water environment, water ecology and water disaster, and is beneficial to improving the comprehensiveness of water resource evaluation.

[0006] The purpose of the present application is realized by the following technical solutions:

[0007] The present application provides a coupling coordination degree analysis method of a water resource-water environment-water ecology-water disaster correlation system, comprising the following steps:

[0008] Step 1, collect and screen the key factors of water resources-water environment-water ecology-water disaster correlation system:

[0009] Determine the scope of the area to be studied, comprehensively sort out the indicators used in the existing research results of water resources, water environment, water ecology and water disasters in the scope of the area to be studied, and screen the key factors of water resources-water environment-water ecology-water disaster correlation system from them;

[0010] Step 2, analyze the spatio-temporal variation characteristics of the key factors:

[0011] The time series of each key factor screened in step 1 is analyzed for trend by methods including slope method, Cox-Stuart test, and Mann-Kendall test;

[0012] The spatial clustering of each key factor is analyzed by using Moran's index;

[0013] Step 3, establish an index evaluation system for the water resources-water environment-water ecology-water disaster correlation system:

[0014] Comprehensively use analysis methods including analytic hierarchy process, entropy weight method, CRITIC weight method, and projection pursuit evaluation method to analyze and obtain four sets of weight distribution results of the water resources, water environment, water ecology, and water disaster systems;

[0015] Step 4, determine the weights of the water resources-water environment-water ecology-water disaster correlation system index system:

[0016] Spearman correlation analysis is performed on the four sets of weight distribution results obtained in step 3, the linear combination coefficient of the correlation significant method is calculated by using game theory combination weighting method, and the most possible weights of the water resources, water environment, water ecology, and water disaster systems are calculated;

[0017] Step 5, analyze the coupling coordination degree of the water resources-water environment-water ecology-water disaster correlation system:

[0018] According to the coupling degree, coordination degree, and coupling coordination degree calculation formulas, the coupling degree, coordination degree, and coupling coordination degree of the water resources-water environment-water ecology-water disaster correlation system are calculated, and QGIS software is used to reveal their spatial distribution characteristics.

[0019] Further, in step 1, the key factors of the water resources-water environment-water ecology-water disaster correlation system include:

[0020] The key factors of the water resources subsystem include per capita water resources, water consumption per ten thousand yuan of GDP, water resources development and utilization rate, per capita water consumption, domestic water consumption, industrial water consumption, agricultural water consumption, and precipitation;

[0021] The key factors of the water environment subsystem include sewage treatment rate, chemical oxygen demand discharge, drinking water quality compliance rate, ammonia nitrogen discharge, proportion of I-III type water quality section, total phosphorus discharge, total industrial wastewater discharge, sediment discharge and sediment concentration;

[0022] The key factors of the water ecological subsystem include ecological water consumption, water and soil loss control area, water system property level, water surface rate and vegetation comprehensive index;

[0023] The key factors of the water disaster subsystem include population affected by flood, direct economic loss caused by flood, crop area affected by flood, reservoir quantity, dike length and total water conservancy construction investment.

[0024] Further, in step 3, the specific steps of the projection pursuit evaluation method include:

[0025] S31, the normalization processing is performed on each key factor obtained in step 1, and the calculation formula is:

[0026]

[0027] In the formula, x'(i,j) and x(i,j) represent the normalized data of the jth key factor before and after the normalization of the ith sample respectively; x max (j) and x min (j) represent the maximum and minimum values of the jth key factor in the sample set respectively.

[0028] S32, the p-dimensional sample data X(i) is integrated into a one-dimensional projection value z(i) with the unit vector A as the projection direction, and the calculation formula is:

[0029]

[0030] In the formula, T represents the transpose of the matrix; a(j) represents the weight size corresponding to the key factor;

[0031] The projection pursuit requires that the local projection points are as dense as possible, and preferably condense into several point groups; and in the whole, the projection point groups are as far apart as possible; the projection index function Q(A) is constructed, and the calculation formula is:

[0032] Q(A)=S z ·D z

[0033] In the formula, S z represents the standard deviation of the projection value; D z represents the local density of the projection value.

[0034] S33, the solution of the projection direction A is converted into the solution of a nonlinear optimization problem, and the problem is:

[0035] maxQ(A) = S z ·D z

[0036]

[0037] S34, solving the nonlinear optimization problem to obtain the best projection direction A * , a in the best projection direction * (j) represents the weight value of the jth key factor.

[0038] Further, step 3 S32 described S z , the calculation formula is:

[0039]

[0040] In the formula, z(j) represents the sample projection value;

[0041] S32 described D z , the calculation formula is:

[0042]

[0043] In the formula, R represents the window radius of local density, R = 0.1S z ; r i,j represents the distance between samples, r i,j = |z(i)-z(j)|; u(t) represents the unit step function,

[0044] Further, in step 4, the game theory combination weighting method specifically includes the following steps:

[0045] S41, using Spearman rank correlation coefficient to measure the consistency degree of the weighting results of the four methods.

[0046] S42, assuming that the basic weight vector set W = {W1, W2, W3, W4} obtained by using the analytic hierarchy process, entropy weight method, CRITIC weight method and projection pursuit evaluation method in step 3, wherein Let α = {α1, α2, α3, α4} be the linear combination coefficient, then W * can be obtained by arbitrary linear combination of these vectors:

[0047]

[0048] In the formula, W1, W2, W3, and W4 represent the basic weight results calculated by the analytic hierarchy process, entropy weight method, CRITIC weight method, and projection pursuit evaluation method, respectively.

[0049] S43, to minimize the deviation of any weight vector in W * The linear combination coefficients are optimized to minimize the deviation of any weight vector in W, and the weight coefficients are determined, and the calculation formula is:

[0050]

[0051] In the formula, α k represents the linear combination coefficient; represents the transpose of any weight vector; represents the transpose of the basic weight vector set.

[0052] S44, according to the matrix differential property, the deviation minimization is converted into the optimal first derivative matrix to obtain the combination optimization coefficient α and normalize it, and the calculation formula is:

[0053]

[0054] In the formula, represents the combined optimization weight.

[0055] S45, the combined weight is calculated, and the calculation formula is:

[0056]

[0057] Further, step 5 specifically includes the following steps:

[0058] S51, the coupling degree C between the water resources, water environment, water ecology, and water disaster subsystems is calculated, and the calculation formula is:

[0059]

[0060] In the formula, U1, U2, U3, U4 respectively represent the development level of the water resources, water environment, water ecology, and water disaster subsystems; a i , b j , c m , d n respectively represent the index weight of the water resources, water environment, water ecology, and water disaster subsystems; w i , x j , y m , z n respectively represent the standardized data of the water resources, water environment, water ecology, and water disaster subsystems; l, q, s, t respectively represent the number of indexes of the water resources, water environment, water ecology, and water disaster subsystems.

[0061] S52, the coordination degree T between the water resources, water environment, water ecology, and water disaster subsystems is calculated, and the calculation formula is:

[0062] T=α1×U1+α2×U2+α3×U3+α4×U4

[0063] In the formula, α1, α2, α3, and α4 represent the weights of the water resources, water environment, water ecology, and water disaster subsystems, respectively.

[0064] S53, the formula for calculating the coupling coordination degree D among the water resources, water environment, water ecology, and water disaster subsystems is:

[0065]

[0066] The advantages of this invention compared to the prior art are as follows:

[0067] Compared with existing water resources-water environment-water ecosystem coupling coordination degree models, this invention introduces a water disaster system, making water resource assessment more comprehensive and fully considering the role of the water disaster system in water resource assessment. It has important reference significance for multi-reservoir group scheduling and water conservancy facility investment in watersheds. At the same time, it optimizes the selection of indicator system and weight allocation calculation, making the coupling coordination degree model evaluation more scientific. Attached Figure Description

[0068] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0069] Figure 1 This is a schematic diagram of the calculation process for the analytical method of coupling coordination degree of the water resources-water environment-water ecology-water disaster association system described in this invention;

[0070] Figure 2 This is a schematic diagram showing the overview and location distribution of the study area in an embodiment of the present invention;

[0071] Figure 3 A schematic diagram of the spatial distribution of total water consumption provided in this embodiment of the invention;

[0072] Figure 4 A schematic diagram illustrating the time series variation of total water consumption provided in this embodiment of the invention;

[0073] Figure 5 A schematic diagram illustrating the trend change of total water consumption according to the Mann-Kendall test provided in this embodiment of the invention. Detailed Implementation

[0074] Example

[0075] like Figure 1 As shown, this embodiment provides a method for analyzing the coupling coordination degree of a water resource-water environment-water ecology-water disaster related system, including the following steps:

[0076] Step 1: Collect and screen key factors of the water resources-water environment-water ecology-water disaster correlation system:

[0077] Determine the scope of the area to be studied, and comprehensively sort out the existing research results of water resources, water environment, water ecology, and water disasters in the scope of the area to be studied, and screen the key factors of the water resources-water environment-water ecology-water disaster correlation system from the selected indexes.

[0078] Specifically, the research results are mainly the results in the published relevant literature, the data of the relevant departments of the state including China Urban Construction Statistical Yearbook, China Urban Statistical Yearbook, China River Silt Bulletin, China Water Conservancy Statistical Yearbook, China Flood and Drought Disaster Prevention Bulletin, etc., the data of the relevant departments of the province including provincial water resources bulletin, provincial statistical yearbook, provincial water conservancy department, and provincial ecological environment department, the data of the relevant departments of the city including municipal water resources bulletin, municipal ecological environment bureau, and municipal ecological environment status bulletin, and network and institutional data including EPS data platform, surface water coverage remote sensing data set, multi-source FVC remote sensing data set, GIMMS NDVI, and PKU GIMMS NDVI.

[0079] The key factors of the water resources subsystem include per capita water resources, water consumption per ten thousand yuan of GDP, water resources development and utilization rate, per capita water consumption, domestic water consumption, industrial water consumption, agricultural water consumption, and precipitation;

[0080] The key factors of the water environment subsystem include sewage treatment rate, chemical oxygen demand discharge, drinking water quality compliance rate, ammonia nitrogen discharge, proportion of I-III type water quality section, total phosphorus discharge, total industrial wastewater discharge, sediment discharge, and sediment concentration;

[0081] The key factors of the water ecology subsystem include ecological water consumption, water loss control area, water system property level, water surface rate, and vegetation comprehensive index;

[0082] The key factors of the water disaster subsystem include population affected by floods, direct economic loss due to floods, crop area affected by floods, reservoir quantity, dike length, and total water conservancy construction investment.

[0083] Step 2, analyze the spatio-temporal variation characteristics of the key factors:

[0084] The time series of each key factor screened in step 1 is analyzed for trend by using methods including slope method, Cox-Stuart test, and Mann-Kendall test;

[0085] The spatial clustering of each key factor is analyzed by using Moran's index.

[0086] Step 3, establish an index evaluation system of the water resources-water environment-water ecology-water disaster correlation system:

[0087] The weight distribution results of four sets of water resources, water environment, water ecology and water disaster systems are obtained by comprehensively using analytic methods including analytic hierarchy process, entropy weight method, CRITIC weight method and projection pursuit evaluation method.

[0088] Specifically, the specific steps of the projection pursuit evaluation method used in the embodiment include:

[0089] S31, each key factor obtained in step 1 is normalized, and the calculation formula is:

[0090]

[0091] In the formula, x'(i,j) and x(i,j) represent the normalized data of the jth key factor and the ith sample before and after normalization respectively; x max (j) and x min (j) represent the maximum and minimum values of the jth key factor in the sample set respectively.

[0092] S32, the p-dimensional sample data X(i) is integrated into a one-dimensional projection value z(i) with the unit vector A as the projection direction, and the calculation formula is:

[0093]

[0094] In the formula, T represents the transpose of the matrix; a(j) represents the weight size corresponding to the key factor.

[0095] The projection pursuit requires that the local projection points are as dense as possible, and preferably condense into several point groups; and in the whole, the projection point groups are as far apart as possible; the projection index function Q(A) is constructed, and the calculation formula is:

[0096] Q(A)=S z ·D z

[0097] In the formula, S z represents the standard deviation of the projection value; D z represents the local density of the projection value.

[0098] The S z in the description is calculated according to the formula:

[0099]

[0100] In the formula, z(j) represents the sample projection value.

[0101] The D z in the description of S32 is calculated according to the formula:

[0102]

[0103] where R represents the window radius of local density, R = 0.1S z ; r i,j represents the distance between samples, r i,j = |z(i)-z(j)|; u(t) represents the unit step function,

[0104] S33, converting the solving of the projection direction A into solving a nonlinear optimization problem, the problem is:

[0105] max Q(A) = S z ·D z

[0106]

[0107] S34, solving the nonlinear optimization problem can obtain the optimal projection direction A * , the weight value of the jth key factor in the optimal projection direction a * (j) represents.

[0108] Step 4, determine the weight of the water resources-water environment-water ecology-water disaster correlation system index system:

[0109] Spearman correlation analysis is performed on the four sets of weight distribution results obtained in step 3, and the linear combination coefficient of the correlation significant method is calculated by using the game theory combination weighting method, and the most possible weight of each system of water resources, water environment, water ecology and water disaster is calculated.

[0110] The game theory combination weighting method specifically includes the following steps:

[0111] S41, the Spearman rank correlation coefficient is used to measure the consistency degree of the four sets of weighting results.

[0112] S42, set the basic weight vector set W = {W1, W2, W3, W4} obtained by using the analytic hierarchy process, entropy weight method, CRITIC weight method and projection pursuit evaluation method in step 3, wherein Let α = {α1, α2, α3, α4} be the linear combination coefficient, then W * can be linearly combined by these vectors as:

[0113]

[0114] In the formula, W1, W2, W3 and W4 represent the basic weight results calculated by the analytic hierarchy process, entropy weight method, CRITIC weight method and projection pursuit evaluation method respectively.

[0115] S43, W *The linear combination coefficients are optimized to determine the weight coefficients, with the objective of minimizing the deviation of any weight vector in W, and the calculation formula is:

[0116]

[0117] In the formula, α k represents the linear combination coefficient; represents the transpose of any weight vector; represents the transpose of the basic weight vector set.

[0118] S44, according to the matrix differential property, the deviation minimization is converted into the optimal first-order derivative matrix to obtain the combination optimization coefficient α and normalize it, and the calculation formula is:

[0119]

[0120] In the formula, represents the combination optimization weight.

[0121] S45, the combination weight is calculated, and the calculation formula is:

[0122]

[0123] Step 5, analyze the coupling coordination degree of the water resources-water environment-water ecology-water disaster correlation system:

[0124] According to the coupling degree, coordination degree, and coupling coordination degree calculation formula, the coupling degree, coordination degree, and coupling coordination degree of the water resources-water environment-water ecology-water disaster correlation system are calculated, and QGIS software is used to reveal the spatial distribution characteristics. Specifically, the following steps are included:

[0125] S51, the coupling degree C between the water resources, water environment, water ecology, and water disaster subsystems is calculated according to the formula:

[0126]

[0127] In the formula, U1, U2, U3, and U4 represent the development levels of the water resources, water environment, water ecology, and water disaster subsystems, respectively; a i , b j , c m , and d n represent the index weights of the water resources, water environment, water ecology, and water disaster subsystems, respectively; w i , x j , y m , and z n represent the standardized data of the water resources, water environment, water ecology, and water disaster subsystems, respectively; l, q, s, and t represent the number of indexes of the water resources, water environment, water ecology, and water disaster subsystems, respectively.

[0128] S52, the coordination degree T between the water resources, water environment, water ecology, and water disaster subsystems is calculated according to the following formula:

[0129] T = a1 * U1 + a2 * U2 + a3 * U3 + a4 * U4

[0130] In the formula, a1, a2, a3, and a4 represent the weights of the water resources, water environment, water ecology, and water disaster subsystems, respectively.

[0131] S53, the coupling coordination degree D between the water resources, water environment, water ecology, and water disaster subsystems is calculated according to the following formula:

[0132]

[0133] Application example:

[0134] The cities of the Shaanxi section of the Yellow River Basin are taken as the research area, including the cities of Xi'an, Tongchuan, Baoji, Xianyang, and Weinan in the Guanzhong region, the cities of Yan'an and Yulin in the northern region of Shaanxi, and the city of Shangluo in the southern region of Shaanxi (). Figure 2 The permanent population and GDP of the research area account for 85% and 90% of the total amount of Shaanxi Province, respectively.

[0135] The method described in the embodiment is used to screen the key factors of the water resources-water environment-water ecology-water disaster system. The indexes selected for the water resources-water environment-water ecology-water disaster system and the weight calculation results are shown in Table 1.

[0136] Table 1. Results of index weight calculation by different weight calculation methods

[0137]

[0138] The trend analysis of the time series of the key factors is performed by using the slope method, Cox-Stuart test, and Mann-Kendall test. The trend change diagram of the Mann-Kendall test of the total water consumption is shown in FIG. 1; the spatial distribution of the key factors is obtained by using the QGIS software, and the spatial distribution diagram of the total water consumption is shown in FIG. 2, and the time series change diagram of the total water consumption is shown in FIG. 3. Figure 5 Figure 3 Figure 4

[0139] Finally, it should be noted that the above merely serves to illustrate the technical solutions of the present application and is not limiting. Although the present application has been described in detail with reference to the preferred arrangement, those skilled in the art should understand that the technical solutions of the present application (such as the use of various formulas, the order of steps, etc.) can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.​​​

Claims

1. A method for analyzing the coupling coordination degree of a water resources-water environment-water ecology-water disaster correlation system, characterized in that, The method includes the following steps: Step 1: Collect and screen key factors of the water resources-water environment-water ecology-water disaster correlation system: The scope of the study area is determined, and the indicators used in the existing research results on water resources, water environment, water ecology, and water disasters within the study area are comprehensively reviewed. From these, the key factors of the water resources-water environment-water ecology-water disaster correlation system are selected. Step 2: Analyze the spatiotemporal variation characteristics of key factors: Trend analysis was performed on the time series of each key factor obtained in step 1 using methods including the slope method, Cox-Stuart test, and Mann-Kendall test. Moran's index analysis was used to obtain the spatial clustering of each key factor; Step 3: Establish an indicator evaluation system for the water resources-water environment-water ecology-water disaster correlation system: By comprehensively utilizing analytical methods including the analytic hierarchy process, entropy weight method, CRITIC weight method, and projective pursuit evaluation method, the weight allocation results of four sets of water resources, water environment, water ecology, and water disaster systems were obtained. Step 4: Determine the weights of the indicator system for the water resources-water environment-water ecology-water disaster correlation: Spearman correlation analysis was performed on the four sets of weight allocation results obtained in step 3. The linear combination coefficients of the significantly correlated methods were calculated using the game theory combination weighting method, and the most likely weights of each system of water resources, water environment, water ecology and water disaster were calculated. The game theory combinatorial weighting method specifically includes the following steps: S41, the Spearman rank correlation coefficient is used to measure the consistency of the weighting results of the four methods; S42, Suppose that the basic weight vector set W = {W1, W2, W3, W4} obtained in step 3 using the analytic hierarchy process (AHP), entropy weight method, CRITIC weight method, and projective pursuit evaluation method is used. Let α = {α1, α2, α3, α4} be the coefficients of the linear combination, then W * These vectors can be arbitrarily combined linearly as follows: In the formula, W1, W2, W3, and W4 represent the basic weight results calculated by the analytic hierarchy process, entropy weight method, CRITIC weight method, and projective pursuit evaluation method, respectively. S43, with W * With the objective of minimizing the deviation of any weight vector in W, the linear combination coefficients are optimized to determine the weight coefficients, and the calculation formula is as follows: In the formula, α k Represents the coefficients of a linear combination; Represents the transpose of any weight vector; Represents the transpose of the basic weight vector set; S44, by applying the properties of matrix differentiation, minimizing the deviation is transformed into the optimal first-order derivative matrix, yielding the combined optimization coefficients α, which are then normalized. The calculation formula is as follows: In the formula, Represents the weights for portfolio optimization; S45, Calculate the portfolio weights using the following formula: Step 5: Analyze the coupling coordination degree of the water resources-water environment-water ecology-water disaster related system: Based on the formulas for calculating coupling degree, coordination degree, and coupling coordination degree, the coupling degree, coordination degree, and coupling coordination degree of the water resources-water environment-water ecology-water disaster related system are calculated, and its spatial distribution characteristics are revealed using QGIS software.

2. The method for analyzing the coupling coordination degree of the water resources-water environment-water ecology-water disaster association system according to claim 1, characterized in that, In step 1, the key factors of the water resources-water environment-water ecology-water disaster correlation system include: Key factors of the water resources subsystem include per capita water resources, water consumption per 10,000 yuan of GDP, water resources development and utilization rate, per capita water consumption, domestic water consumption, industrial water consumption, agricultural water consumption, and precipitation. Key factors of the water environment subsystem include wastewater treatment rate, chemical oxygen demand (COD) discharge, drinking water quality compliance rate, ammonia nitrogen discharge, proportion of Class I-III water quality sections, total phosphorus discharge, total industrial wastewater discharge, sediment transport, and sediment content. Key factors of the aquatic ecosystem subsystem include ecological water consumption, area of ​​soil and water conservation, water system generality level, water surface ratio, and vegetation comprehensive index. Key factors in the flood disaster subsystem include the number of people affected by floods, the direct economic losses caused by floods, the area of ​​crops affected by floods, the number of reservoirs, the length of dikes, and the total investment in water conservancy construction.

3. The method for analyzing the coupling coordination degree of the water resources-water environment-water ecology-water disaster association system according to claim 1, characterized in that, Step 3, the specific steps of the projection pursuit evaluation method include: S31, Normalize the key factors obtained in step 1. The calculation formula is as follows: In the formula, x'(i,j) and x(i,j) represent the data of the j-th key factor in the i-th sample before and after normalization, respectively; x max (j) and x min (j) represent the maximum and minimum values ​​of the j-th key factor in the sample set, respectively; S32, Project the p-dimensional sample data X(i) with the unit vector A as the projection direction to obtain the one-dimensional projection value z(i). The calculation formula is as follows: In the formula, T represents the transpose of the matrix; a(j) represents the weight of the key factor. Construct the projection index function Q(A), and calculate it using the following formula: Q(A)=S z ·D z In the formula, S z D represents the standard deviation of the projected values. z Local density representing the projected value; S33 transforms the solution for the projection direction into a nonlinear optimization problem, the problem being: maxQ(A)=S z ·D z S34, Solving the nonlinear optimization problem yields the optimal projection direction A. * 'a' in the optimal projection direction * (j) represents the weight value of the j-th key factor.

4. The method for analyzing the coupling coordination degree of the water resources-water environment-water ecology-water disaster association system according to claim 3, characterized in that, In step 3, S32, the S z The calculation formula is: In the formula, z(j) represents the sample projection value, and n represents the number of samples; S32 as described in D z The calculation formula is: In the formula, R represents the window radius of the local density, R = 0.1S z ;r i,j r represents the distance between samples i,j =|z(i)-z(j)|; u(t) represents the unit step function.

5. The method for analyzing the coupling coordination degree of the water resources-water environment-water ecology-water disaster association system according to claim 1, characterized in that, Step 5 specifically includes the following steps: S51, the formula for calculating the coupling degree C among the water resources, water environment, water ecology, and water disaster subsystems is: In the formula, U1, U2, U3, and U4 represent the development levels of the water resources, water environment, water ecology, and water disaster subsystems, respectively; a i b j c m d n The indicator weights represent the weights of the water resources, water environment, water ecology, and water disaster subsystems, respectively; w i x j y m z n These represent the standardized data for the water resources, water environment, water ecology, and water disaster subsystems, respectively; l, q, s, and t represent the number of indicators for the water resources, water environment, water ecology, and water disaster subsystems, respectively. S52, the formula for calculating the coordination degree T among the water resources, water environment, water ecology, and water disaster subsystems is: T=β1×U1+β2×U2+β3×U3+β4×U4 In the formula, β1, β2, β3, and β4 represent the weights of the water resources, water environment, water ecology, and water disaster subsystems, respectively. S53, the formula for calculating the coupling coordination degree D among the water resources, water environment, water ecology, and water disaster subsystems is:

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