Construction method and system of water resource bearing capacity comprehensive evaluation index system

Abstract

The invention discloses a construction method and system for a comprehensive evaluation index system of water resource bearing capacity, and the method comprises the steps: analyzing the water resource bearing capacity, and forming a system framework of the evaluation index system; preliminarily constructing a corresponding evaluation index system according to a system framework and existing research; performing normalization processing and correlation and information contribution rate analysis on the preliminarily constructed evaluation index data set to realize screening and optimization of evaluation indexes; and determining a comprehensive evaluation index system and an index weight of the water resource bearing capacity. According to the method, multiple factors are comprehensively considered, indexes with high correlation and low relative importance level are screened through a correlation screening method, the redundancy of index information and the influence of subjective factors in the index screening process are avoided, and the scientificity and rationality of evaluation index system construction are improved.

Description

Technical Field

[0001] The present invention relates to the field of water resource carrying capacity, and in particular to a method and system for constructing a comprehensive evaluation index system for water resource carrying capacity. Background Art

[0002] Faced with the current situation of water shortage and fragile water ecology, how to coordinate the relationship between economic and social development and water resource utilization and ecological protection has become a key issue that needs to be solved urgently. As an important indicator to measure the coordinated development of regional water resources to support the economy, society and ecology, the evaluation research of water resource carrying capacity is a necessary basic work to solve this problem. Water resource carrying capacity is a complex and comprehensive system involving multiple fields such as resources, environment, society and economy. Constructing a scientific and reasonable evaluation index system is the key to accurately evaluate the regional water resource carrying capacity. The effectiveness of the evaluation index system often depends on whether the selected indicators are scientific and reasonable and can truly reflect the operation status of the system. Therefore, in the process of constructing the index system, the principles of scientificity, systematicity, dynamism, comparability and feasibility should be followed. The selected evaluation indicators should cover multiple aspects of water resource carrying capacity and have a certain degree of representativeness. The correlation between the indicators should be kept low to avoid the redundancy of indicator information. Summary of the invention

[0003] The present invention provides a method and system for constructing a comprehensive evaluation index system for water resources carrying capacity. The method screens indicators based on multiple aspects, avoids the redundancy of indicator information and the influence of subjective factors in the indicator screening process, and improves the scientificity and rationality of the construction of the evaluation index system.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for constructing a comprehensive evaluation index system for water resources carrying capacity comprises the following steps:

[0006] Step 1: Analyze the water resources carrying capacity and form a systematic framework of the evaluation index system;

[0007] Step 2: Based on the system framework and existing research, initially construct a corresponding evaluation index system;

[0008] Step 3: Normalize the initially constructed evaluation index data set, analyze its correlation and information contribution rate, and select and optimize the evaluation index;

[0009] Step 4: Determine the comprehensive evaluation index system and index weights for water resources carrying capacity.

[0010] Furthermore, in step 1, the present invention determines that the system framework of water resources carrying capacity includes a target layer, a criterion layer and an indicator layer. The target layer is used to determine the direction of constructing the evaluation index. The criterion layer is to determine the specific dimension according to the relationship with the demand and utilization of water resources under the determined target layer direction. The indicator layer is to select data indicators that can reflect the corresponding dimension under the specific dimension.

[0011] Furthermore, in step 2, the purpose of constructing a water resources carrying capacity index system is to achieve harmonious development between man and nature by resolving the contradiction between social and economic development and water resources and environment. This means that the constructed water resources carrying capacity evaluation index system reflects the water resources level and ecological environment status of the region on the one hand, and can detect whether human activities are within the carrying capacity of the water resources environment in the region on the other hand. The construction of the water resources carrying capacity evaluation index system should focus on four aspects: the supporting capacity of water resources, the level of ecological environment, and the social and economic development status. Therefore, the present invention determines that the target layer of the evaluation index system is composed of four subsystems: water resources, society, economy, and ecological environment.

[0012] Furthermore, in step 2, the water resources subsystem reflects the support capacity of regional water resources for human activities, and the quality and quantity of regional water resources determine the degree of exploitation and utilization of regional water resources. Therefore, the three dimensions of natural endowment of water resources, water production and supply capacity, and development and utilization status are used as the criterion layer. Five indicators, namely, precipitation, total water resources, per capita water resources, surface water resources per unit area, and groundwater resources per unit area, are selected to reflect the natural endowment of regional water resources; three indicators, namely, water production modulus, water production coefficient, and water supply modulus, are selected to reflect the water production and supply capacity of the region; three indicators, namely, water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate, are selected to reflect the current status of regional water resources development and utilization.

[0013] Furthermore, in step 2, the impact of the social subsystem on water resources mainly comes from the population. The structure and quantity of the population, the lifestyle of residents, the living standards and other factors are directly related to the utilization of water resources and have an important impact on the sustainable utilization of regional water resources. Therefore, the two dimensions of regional population structure and quantity and domestic water use level are used as the criterion layer. Five indicators, namely population density, urbanization rate, permanent population at the end of the year, natural population growth rate, and number of students in colleges and universities, are selected to reflect the population structure and quantity of the region; five indicators, namely, daily domestic water consumption per capita in urban areas, per capita comprehensive water consumption, proportion of domestic water use, urban water penetration rate, and per capita retail sales of social consumer goods, are selected to reflect the domestic water use level.

[0014] Furthermore, in step 2, the economic subsystem is an important part of the water resources carrying capacity evaluation. The scale of economic development and the industrial structure directly affect the demand for water resources and the development and utilization of water resources. In addition, the higher the level of economic development, the higher the level of water resource reuse and pollution control. Therefore, the two dimensions of economic development scale and economic development water use efficiency are used as the criterion layer. Seven indicators, including GDP, proportion of secondary industry, proportion of tertiary industry, per capita GDP, per capita disposable income of urban residents, proportion of industrial water use, and R&D expenditure, are selected to reflect the scale of regional economic development; five indicators, including per mu water use for farmland irrigation, water use per 10,000 yuan of industrial added value, water use per 10,000 yuan of GDP, water consumption rate, and cultivated land irrigation rate, are selected to reflect the water use efficiency of economic development.

[0015] Furthermore, in step 2, the ecological environment subsystem and the water resources system are both interdependent and mutually constrained. On the one hand, the ecological environment system plays an important role in conserving water sources and maintaining water and soil; on the other hand, the rapid development of social economy has led to urbanization expansion, occupation of cultivated land, industrial and agricultural and domestic pollutant emissions, etc., which have put pressure on the ecological environment. Therefore, the three dimensions of ecological environment status, ecological environment governance level and ecological environment pressure are used as the criterion layer. Five indicators, namely ecological water use rate, forest coverage rate, proportion of cultivated land area, green coverage rate and per capita park green space area, are selected to reflect the regional ecological environment status; two indicators, namely sewage treatment rate and harmless treatment rate of domestic waste, are selected to show the regional ecological environment governance level; three indicators, namely sewage discharge, sewage discharge per 10,000 yuan of GDP and fertilizer application, are selected to reflect the regional ecological environment pressure.

[0016] Furthermore, in step 3, the indicator data in the water resources carrying capacity evaluation indicator system are normalized to eliminate the differences in the positive, reverse, dimension and magnitude between different indicator data. The calculation formula for the normalization is as follows:

[0017] Normalization of positive indicator data:

[0018]

[0019] Reverse indicator data standardization:

[0020]

[0021] Where: x ij represents the jth indicator of the i-th evaluation object; y ij Represents x ij The standardized value of max j (x ij ) and min j (x ij ) represent x ij The maximum and minimum values ​​of .

[0022] Furthermore, in step 3, the indicators with strong correlation in the system are eliminated through correlation analysis. The screening principle is as follows: Pearson bivariate correlation analysis is performed on the indicators of each target layer to eliminate the indicators with multicollinearity in each target layer. Specifically: (1) When the absolute value of the correlation coefficient between a certain indicator and two or more other indicators is greater than 0.8, the latter is deleted in principle; (2) When the absolute value of the correlation coefficient between two indicators is greater than 0.8, the indicators with high frequency used in relevant references are retained in principle; the correlation coefficient is calculated using the Pearson correlation coefficient method, and the calculation formula is as follows:

[0023]

[0024] In the formula: r represents the correlation coefficient; x i and i Indicates two different sets of indicator data under the same subsystem; and Represents the mean of two groups of indicator data; n is the total number of samples of the indicator.

[0025] Furthermore, in step 3, based on the information contribution rate analysis, the indicators with low explanation level for the original indicator set are deleted, and the cumulative contribution rate threshold R0 is set. The calculation method of the cumulative information contribution rate is as follows:

[0026] (1) Validity test of indicator data

[0027] Kaiser-Meyer-Olkin (KMO) test and Bartlett sphericity test were performed on the indicator data to ensure that the indicator system had structural validity; when the KMO statistic was greater than 0.5 and the significance probability of the Bartlett sphericity test statistic was less than 0.05, the applicability test was passed; if it failed the test, the relevant indicator system needed to be re-determined;

[0028] (2) Determine the key factors that approximately reflect all the information of the original indicator set

[0029] The principal component analysis method is used to extract key factors with eigenvalues ​​greater than 1. If the cumulative variance contribution rate of the extracted key factors is greater than 70%, it is considered that these factors can approximately reflect all the information of the original indicator set. The mathematical calculation of the eigenvalue and cumulative variance contribution rate of the indicator matrix are shown in formula (4) and formula (5) respectively:

[0030] X T X-λ j E n |=0 (4)

[0031] Where: X is the normalized indicator matrix; λ jis the matrix eigenvalue; j is the number of factors; E n is the n-th order identity matrix;

[0032]

[0033] Where: V p is the cumulative variance contribution rate of p factors; ω j is the variance contribution rate of the jth factor;

[0034] (3) Calculate the factor loading matrix

[0035] The factor loading matrix is ​​constructed based on the extracted key factors, and the calculation formula is as follows:

[0036]

[0037] Where: A is the factor loading matrix; a ij is the jth factor loading of the ith indicator; ξ p is the orthonormalized eigenvector of the pth eigenvalue;

[0038] (4) Calculate the information contribution rate of the indicator

[0039]

[0040] Where: C i is the information contribution rate of the ith indicator; a ij 2 is the information size of the i-th indicator contained in the j-th factor;

[0041] (5) Screening indicators based on information contribution rate

[0042]

[0043] Where: R s is the cumulative information contribution rate of s indicators; C ni The information contribution rate of n indicators is sorted from large to small, and the information contribution rate of the i-th indicator is ranked; if the cumulative information contribution rate of the indicator R s Satisfy R s-1 <R0≤R s , then retain the indicators whose information contribution rates are ranked in the top s order from large to small.

[0044] Furthermore, in step 4, the weights of the indicators at each level in each subsystem of the water resources carrying capacity evaluation indicator system are determined by indicator screening, and the calculation formula of the indicator weights is as follows:

[0045]

[0046] In the formula: the indicators retained after index screening are X1, X2, X3, ..., X k , and their corresponding information contribution rates are I1, I2, I3, ..., I k The importance of each subsystem of water resources carrying capacity is consistent, and the corresponding weight of each indicator in the overall comprehensive evaluation index system of water resources carrying capacity is obtained.

[0047] A system for constructing a comprehensive evaluation index system for water resources carrying capacity includes a system analysis unit, a data collection unit, a data preprocessing unit, an index screening unit, and an index and weight determination unit; wherein:

[0048] The system analysis unit is used to analyze the water resource carrying capacity and determine the composition of the system framework of the water resource carrying capacity evaluation index system; wherein the system framework includes a target layer, a criterion layer and an indicator layer, wherein the target layer is used to determine the direction of constructing the evaluation index, the criterion layer is used to determine the specific dimension according to the relationship between the demand and utilization of water resources under the determined target layer direction, and the indicator layer is used to select the indicator that can reflect the corresponding dimension under the specific dimension;

[0049] The data collection unit is used to collect and obtain the data of each indicator preliminarily determined in the system analysis unit;

[0050] The data preprocessing unit is used to normalize the indicator data collected by the data collection unit;

[0051] The indicator screening unit is used to perform correlation analysis and information contribution rate analysis on the indicator data after normalization processing by the data preprocessing unit to obtain the indicator to be deleted;

[0052] The indicator and weight determination unit determines the weight of each indicator after screening based on the screening result of the indicator screening unit and the weight calculation method, and finally outputs a comprehensive evaluation indicator system for water resources carrying capacity.

[0053] By adopting the above technical means, the beneficial effects and advantages achieved by the present invention are:

[0054] (1) The present invention fully analyzes the connotation of the water resources carrying capacity system in the construction of the water resources carrying capacity evaluation index system, and the index selection is comprehensive, dynamic and timely;

[0055] (2) The present invention eliminates indicators with strong correlation and low relative importance through the correlation screening method, avoids the redundancy of indicator information and the influence of subjective factors in the indicator screening process, and improves the scientificity and rationality of the construction of the evaluation indicator system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1A technical roadmap for the method of the present invention;

[0057] Figure 2 This is a schematic diagram of the water resource carrying capacity system of the present invention;

[0058] Figure 3 The structure diagram of the construction system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0059] Taking a specific river basin as an example, combined with the attached drawings, a method and system for constructing a comprehensive evaluation index system for water resources carrying capacity is further described. The method comprehensively considers various factors and implements the screening of indicators with strong correlation and low relative importance through a specific screening method, avoiding the redundancy of indicator information and the influence of subjective factors in the indicator screening process. The system includes a system analysis unit, a data collection unit, a data preprocessing unit, an indicator screening unit, and an indicator and weight determination unit. The present invention can effectively improve the scientificity and rationality of the water resources carrying capacity evaluation system.

[0060] According to a specific embodiment of the present invention, a method for constructing a comprehensive evaluation index system for water resources carrying capacity is as follows: Figure 1 , specifically including the following steps:

[0061] Step 1: Analyze the connotation of water resources carrying capacity based on the concept of ecological civilization and form a systematic framework of the evaluation index system;

[0062] The present invention is based on the existing water resources carrying capacity theory, closely combines the specific application of system theory in the field of water resources carrying capacity and domestic and foreign research progress, conducts an in-depth analysis of the system structure of water resources carrying capacity, and determines that the system structure of the water resources carrying capacity evaluation index system consists of a target layer, a criterion layer, and an indicator layer. The purpose of constructing a water resources carrying capacity index system based on the concept of ecological civilization is to achieve harmonious development between man and nature by resolving the contradiction between social and economic development and resources and the environment. This means that the constructed water resources carrying capacity evaluation index system reflects the water resources level and ecological environment status of the region on the one hand, and on the other hand, it can detect whether human activities are within the carrying capacity of regional resources and the environment, thereby measuring the coordinated development of the region. Therefore, the target layer of the evaluation index system in the present invention consists of four subsystems: water resources, society, economy, and ecological environment. Figure 2 shown.

[0063] Step 2: Based on the system framework and existing literature research, initially construct the corresponding evaluation index system;

[0064] The water resources subsystem reflects the ability of regional water resources to support human activities, and the quality and quantity of regional water resources determine the degree to which regional water resources can be developed and utilized. Therefore, the three dimensions of natural endowment of water resources, water production and supply capacity, and development and utilization status are used as the criterion layer. Five indicators, namely precipitation, total water resources, per capita water resources, surface water resources per unit area, and groundwater resources per unit area, are selected to reflect the natural endowment of regional water resources; three indicators, namely water production modulus, water production coefficient, and water supply modulus, are selected to reflect the water production and supply capacity of the region; three indicators, namely water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate, are selected to reflect the current status of regional water resources development and utilization;

[0065] The impact of the social subsystem on water resources mainly comes from the population. The structure and quantity of the population, the lifestyle of residents, the living standards and other factors are directly related to the use of water resources and have an important impact on the sustainable use of regional water resources. Therefore, the two dimensions of regional population structure and quantity and domestic water use level are used as the criterion layer. Five indicators, including population density, urbanization rate, permanent population at the end of the year, natural population growth rate, and number of students in colleges and universities, are selected to reflect the population structure and quantity of the region; five indicators, including daily domestic water consumption per capita in urban areas, per capita comprehensive water consumption, proportion of domestic water use, urban water penetration rate, and per capita retail sales of social consumer goods, are selected to reflect the level of domestic water use;

[0066] The economic subsystem is an important part of the evaluation of water resources carrying capacity. The scale of economic development and the industrial structure directly affect the demand for water resources and the development and utilization of water resources. In addition, the higher the level of economic development, the higher the level of water resource reuse and pollution control. Therefore, the two dimensions of economic development scale and economic development water use efficiency are used as the criterion layer. Seven indicators, including GDP, proportion of secondary industry, proportion of tertiary industry, per capita GDP, per capita disposable income of urban residents, proportion of industrial water use, and R&D expenditure, are selected to reflect the scale of regional economic development; five indicators, including per mu water use for farmland irrigation, water use per 10,000 yuan of industrial added value, water use per 10,000 yuan of GDP, water consumption rate, and cultivated land irrigation rate, are selected to reflect the water use efficiency of economic development;

[0067] The ecological environment subsystem and the water resources system are both interdependent and mutually constrained. On the one hand, the ecological environment system plays an important role in conserving water sources and maintaining water and soil; on the other hand, the rapid development of social economy has led to urbanization expansion, occupation of cultivated land, industrial and agricultural and domestic pollutant emissions, etc., which have put pressure on the ecological environment. Therefore, the three dimensions of ecological environment status, ecological environment governance level and ecological environment pressure are used as the criterion layer. Five indicators, namely ecological water use rate, forest coverage rate, proportion of cultivated land area, green coverage rate and per capita park green space area, are selected to reflect the regional ecological environment status; two indicators, namely sewage treatment rate and harmless treatment rate of domestic waste, are selected to reflect the regional ecological environment governance level; three indicators, namely sewage discharge, sewage discharge per 10,000 yuan of GDP and fertilizer application, are selected to reflect the regional ecological environment pressure.

[0068] Step 3: Normalize the initially constructed evaluation index data set to eliminate the differences in the positive, negative, dimension and magnitude between different index data. The calculation formula for normalization is as follows:

[0069] Normalization of positive indicator data:

[0070]

[0071] Reverse indicator data standardization:

[0072]

[0073] Where: x ij represents the jth indicator of the i-th evaluation object; y ij Represents x ij The standardized value of j (x ij ) and min j (x ij ) represent x ij The maximum and minimum values ​​of .

[0074] Step 4: Based on the normalized indicator data, the indicators with strong correlation in the system are eliminated through correlation analysis. The screening principle is as follows: Pearson bivariate correlation analysis is performed on the indicators of each target layer to eliminate the indicators with multicollinearity in each target layer. Specifically: (1) When the absolute value of the correlation coefficient between a certain indicator and two or more other indicators is greater than 0.8, the latter indicators are deleted in principle; (2) When the absolute value of the correlation coefficient between two indicators is greater than 0.8, the indicators frequently used in the relevant references are retained in principle; the correlation coefficient is calculated using the Pearson correlation coefficient method, and the calculation formula is as follows:

[0075]

[0076] In the formula: r represents the correlation coefficient; xi and i Indicates two different sets of indicator data under the same subsystem; and Represents the mean of two groups of indicator data; n is the total number of samples of the indicator.

[0077] Based on the above screening principles, a total of 9 indicators were deleted, as shown in Table 1.

[0078] Table 1 Index screening results based on correlation coefficient analysis

[0079]

[0080] Step 5: Based on the information contribution rate analysis, further delete the indicators with low explanation level for the original indicator set. The threshold of the cumulative contribution rate R0 is set to 0.7. The calculation method of the cumulative information contribution rate is as follows:

[0081] (1) Validity test of indicator data

[0082] Kaiser-Meyer-Olkin (KMO) test and Bartlett sphericity test were performed on the indicator data to ensure that the indicator system had structural validity; when the KMO statistic was greater than 0.5 and the significance probability of the Bartlett sphericity test statistic was less than 0.05, the applicability test was passed;

[0083] (2) Determine the key factors that approximately reflect all the information of the original indicator set

[0084] The principal component analysis method is used to extract key factors with eigenvalues ​​greater than 1. If the cumulative variance contribution rate of the extracted key factors is greater than 70%, it is considered that these factors can approximately reflect all the information of the original indicator set. The mathematical calculation of the eigenvalue and cumulative variance contribution rate of the indicator matrix are shown in formula (4) and formula (5) respectively:

[0085] |X T X-λ j E n |=0 (4)

[0086] Where: X is the normalized indicator matrix; λ j is the matrix eigenvalue; j is the number of factors; E n is the n-th order identity matrix;

[0087]

[0088] Where: V p is the cumulative variance contribution rate of p factors; ω j is the variance contribution rate of the jth factor;

[0089] (3) Calculate the factor loading matrix

[0090] The factor loading matrix is ​​constructed based on the extracted key factors, and the calculation formula is as follows:

[0091]

[0092] Where: A is the factor loading matrix; a ij is the jth factor loading of the ith indicator; ξ p is the orthonormalized eigenvector of the pth eigenvalue;

[0093] (4) Calculate the information contribution rate of the indicator

[0094]

[0095] Where: C i is the information contribution rate of the ith indicator; a ij 2 is the information size of the i-th indicator contained in the j-th factor;

[0096] (5) Screening indicators based on information contribution rate

[0097]

[0098] Where: R s is the cumulative information contribution rate of s indicators; C ni The information contribution rate of n indicators is sorted from large to small, and the information contribution rate of the i-th indicator is ranked; if the cumulative information contribution rate of the indicator R s Satisfy R s-1 <R0≤R s , then retain the indicators whose information contribution rates are ranked in the top s order from large to small.

[0099] The KMO and Bartlett tests were performed on the index data of each subsystem after correlation analysis to verify the validity of the index data. The test results are shown in Table 2. It can be seen that the KMO values ​​of the indicators of each subsystem of water resources carrying capacity are all greater than 0.5, and the significance probability of the Bartlett spherical test statistics is less than 0.05, indicating that each indicator has structural validity and is suitable for factor analysis.

[0100] Table 2 KMO and Bartlett sphericity test of water resources carrying capacity subsystem

[0101]

[0102]

[0103] Based on the information contribution rate analysis method, the information contribution rate of each indicator is calculated, and the information contribution rate of each indicator is arranged in order from large to small to obtain the corresponding cumulative information contribution rate. Taking the water resources subsystem as an example, the calculation results of the cumulative information contribution rate of the indicator are shown in Table 3. The threshold of the cumulative contribution rate R0 is 0.7, so the indicators with the top six cumulative information contribution rates in the water resources subsystem are retained. Through indicator screening, the weights of the indicators at each level in each subsystem of the water resources carrying capacity evaluation indicator system are determined. The calculation formula of the indicator weight is as follows:

[0104]

[0105] In the formula: the indicators retained after index screening are X1, X2, X3, ..., X k , and their corresponding information contribution rates are I1, I2, I3, ..., I k .

[0106] Table 3 Screening and weighting of water resources subsystem indicators

[0107]

[0108] Similarly, the indicators of retention and deletion and the weight calculation results of the other three subsystems are obtained in turn, as shown in Table 4.

[0109] Table 4 Results of screening and weighting of indicators for social, economic and ecological environment subsystems

[0110]

[0111]

[0112] According to a specific embodiment of the present invention, in a system for constructing a comprehensive evaluation index system for water resources carrying capacity, as Figure 3 As shown, the system analysis unit is used to analyze the water resource carrying capacity and determine the composition of the system framework of the water resource carrying capacity evaluation index system; the user determines the target layer according to the analysis of the water resource carrying capacity, and determines the specific dimensions closely related to the demand and utilization of water resources in the direction with each target layer as the direction, that is, determines the criterion layer, and then selects the indicators that can reflect the corresponding dimensions under each criterion layer, that is, under the specific dimension;

[0113] The data collection unit is used to collect and obtain the data of each indicator preliminarily determined in the system analysis unit;

[0114] The data preprocessing unit is used to normalize the indicator data collected by the data collection unit; the purpose of this processing is to eliminate the differences in the positive, reverse, dimension and magnitude of the indicator data, which is specifically achieved by the method of step 3 as described above;

[0115] The indicator screening unit is used to perform correlation analysis and information contribution rate analysis on the indicator data normalized by the data preprocessing unit to obtain the indicators to be deleted; specifically: based on the normalized indicator data, the indicators of each subsystem of the water resources carrying capacity are sequentially subjected to Pearson bivariate correlation analysis to eliminate the indicators with multicollinearity in each subsystem, which is specifically implemented by the method of step 4 above; then, based on the information contribution rate analysis, the indicators with low explanation level for the original indicator set are further deleted, which is specifically implemented by the method of step 5 above;

[0116] The indicator and weight determination unit determines the weights of each indicator after screening, as well as the weight of the target layer, based on the screening results of the indicator screening unit and the weight calculation method. Specifically, this can be achieved using the method in step 6 above; and finally outputs a comprehensive evaluation indicator system for water resources carrying capacity.

[0117] For the above specific basin example, the present invention normalized the initially constructed water resources carrying capacity evaluation index system, eliminated 19 evaluation indicators based on correlation analysis and information contribution rate analysis, and finally determined a water resources carrying capacity evaluation index system consisting of 4 target layers, 10 criterion layers and 24 indicator layers, as shown in Table 5. The importance of each subsystem of water resources carrying capacity is consistent, and the weight values ​​of the four subsystems are all 0.25, and the weight results of the 24 indicators can be calculated accordingly.

[0118] Table 5 Water resources carrying capacity evaluation index system

[0119]

[0120] The embodiments described above are only specific implementations of the present invention, but the protection scope of the present invention is not limited thereto, and any specific deformation, replacement and combination made by any technician familiar with the field within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for constructing a comprehensive evaluation index system for water resources carrying capacity, characterized in that: The specific steps include: Step 1: Analyze the water resources carrying capacity and form a systematic framework of the evaluation index system; Step 2: Based on the system framework and existing research, initially construct a corresponding evaluation index system; Step 3: Normalize the initially constructed evaluation index data set, analyze its correlation and information contribution rate, and select and optimize the evaluation index; Step 4: Determine the comprehensive evaluation index system and index weights for water resources carrying capacity.

2. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1 is characterized in that: In step 1, the system framework for determining the evaluation index system according to the water resources carrying capacity includes a target layer, a criterion layer and an indicator layer, wherein the target layer is used to determine the direction of constructing the evaluation index, the criterion layer is to determine the specific dimensions according to the relationship with the demand and utilization of water resources under the direction of the target layer, and the indicator layer is to select indicators that can reflect the corresponding dimensions under the specific dimensions.

3. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1 is characterized in that: In the step 2, according to the system framework and existing research, combined with the requirements of the three red lines of the most stringent water resources management system, it is determined that the comprehensive evaluation index system of water resources carrying capacity includes four target layers: water resources subsystem, social subsystem, economic subsystem, and ecological environment subsystem; the water resources subsystem target layer sets three dimensions of natural endowment of water resources, water production and supply capacity, and development and utilization status as the criterion layer; the social subsystem target layer uses the structure and number of regional population and the level of domestic water use as the criterion layer; the economic subsystem target layer uses the scale of economic development and the efficiency of water use for economic development as the criterion layer; the ecological environment subsystem target layer uses the three dimensions of ecological environment status, ecological environment governance level and ecological environment pressure as the criterion layer.

4. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1, characterized in that: In step 3, the indicator data is normalized to eliminate the differences in the positive, negative, dimension and magnitude between different indicator data. The calculation formula for the normalization is as follows: Normalization of positive indicator data: Reverse indicator data standardization: Where: x ij represents the jth indicator data of the i-th evaluation object; y ij Represents x ij The standardized value of max j (x ij ) and min j (x ij ) represent x ij The maximum and minimum values ​​of .

5. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1, characterized in that: In step 3, the indicators with strong correlation in the system are eliminated through correlation analysis. The screening principle is as follows: Pearson bivariate correlation analysis is performed on the indicators of each target layer to eliminate the indicators with multicollinearity in each target layer. Specifically: (1) When the absolute value of the correlation coefficient between a certain indicator and two or more other indicators is greater than 0.8, the latter is deleted in principle; (2) When the absolute value of the correlation coefficient between two indicators is greater than 0.8, the indicator with high frequency used in the relevant references is retained in principle; the correlation coefficient is calculated using the Pearson correlation coefficient method, and the calculation formula is as follows: In the formula: r represents the correlation coefficient; x i and i Indicates two different sets of indicator data under the same subsystem; and Represents the mean of two groups of indicator data; n is the total number of samples of the indicator.

6. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1, characterized in that: In step 3, based on the information contribution rate analysis, the indicators with low explanation level for the original indicator set are deleted, and the cumulative contribution rate threshold R0 is set. The calculation method of the cumulative information contribution rate is as follows: (1) Validity test of indicator data Kaiser-Meyer-Olkin (KMO) test and Bartlett sphericity test were performed on the indicator data to ensure that the indicator system had structural validity; when the KMO statistic was greater than 0.5 and the significance probability of the Bartlett sphericity test statistic was less than 0.05, the applicability test was passed; If the inspection fails, the relevant indicator system needs to be re-determined; (2) Determine the key factors that approximately reflect all the information of the original indicator set The principal component analysis method is used to extract key factors with eigenvalues ​​greater than 1. If the cumulative variance contribution rate of the extracted key factors is greater than 70%, it is considered that these factors can approximately reflect all the information of the original indicator set. The calculation methods of the eigenvalue and cumulative variance contribution rate of the indicator matrix are shown in formula (4) and formula (5) respectively: |X T X-L j E n |=0 (4) Where: X is the normalized indicator matrix; λ j is the matrix eigenvalue; j is the number of factors; E n is the n-th order identity matrix; Where: V p is the cumulative variance contribution rate of p factors; ω j is the variance contribution rate of the jth factor; (3) Calculate the factor loading matrix The factor loading matrix is ​​constructed based on the extracted key factors, and the calculation formula is as follows: Where: A is the factor loading matrix; a ij is the jth factor loading of the ith indicator; ξ p is the orthonormalized eigenvector of the pth eigenvalue; (4) Calculate the information contribution rate of the indicator Where: C i is the information contribution rate of the ith indicator; a ij 2 is the information size of the i-th indicator contained in the j-th factor; (5) Screening indicators based on information contribution rate Where: R s is the cumulative information contribution rate of s indicators; C ni The information contribution rate of n indicators is sorted from large to small, and the information contribution rate of the i-th indicator is ranked; if the cumulative information contribution rate of the indicator R s Satisfy R s-1 <R0≤R s , then retain the indicators whose information contribution rates are ranked in the top s order from large to small.

7. The method for constructing a comprehensive evaluation index system for water resources carrying capacity according to claim 1, characterized in that: In step 4, the weights of the indicators at each level in each subsystem of the water resources carrying capacity evaluation indicator system are determined through indicator screening. The calculation formula of the indicator weight is as follows: In the formula: the indicators retained after index screening are X1, X2, X3, ..., X k , and their corresponding information contribution rates are I1, I2, I3, ..., I k The importance of each subsystem of water resources carrying capacity is consistent, and the corresponding weight of each indicator in the overall comprehensive evaluation index system of water resources carrying capacity is obtained.

8. A system for constructing a comprehensive evaluation index system for water resources carrying capacity, characterized in that: It includes a system analysis unit, a data collection unit, a data preprocessing unit, an indicator screening unit, and an indicator and weight determination unit; wherein: The system analysis unit is used to analyze the water resource carrying capacity and determine the composition of the system framework of the water resource carrying capacity evaluation index system; wherein the system framework includes a target layer, a criterion layer and an indicator layer, wherein the target layer is used to determine the direction of constructing the evaluation index, the criterion layer is used to determine the specific dimension according to the relationship between the demand and utilization of water resources under the determined target layer direction, and the indicator layer is used to select the indicator that can reflect the corresponding dimension under the specific dimension; The data collection unit is used to collect and obtain the data of each indicator preliminarily determined in the system analysis unit; The data preprocessing unit is used to normalize the indicator data collected by the data collection unit; The indicator screening unit is used to perform correlation analysis and information contribution rate analysis on the indicator data after normalization processing by the data preprocessing unit to obtain the indicator to be deleted; The indicator and weight determination unit determines the weight of each indicator after screening based on the screening result of the indicator screening unit and the weight calculation method, and finally outputs a comprehensive evaluation indicator system for water resources carrying capacity.

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