A multi-index-based river recovery effect evaluation method

Abstract

The application discloses a kind of river recovery effect evaluation methods based on multi-index, comprising: constructing the five-dimensional evaluation index system of river recovery effect evaluation, and collecting the evaluation index data corresponding to each dimension in five-dimensional evaluation index system;Build the measurement model representing the mapping of evaluation index and latent variable, the structure model representing the causal relationship between latent variables, and coupled geographic weighted regression GWR model, calculate the spatial dynamic weight of observation index;Set the level of river recovery effect, generate the cloud membership matrix of each index corresponding to recovery level;Calculate the correlation degree of observation index and river recovery effect level;Calculate the comprehensive correlation degree of each river section and river recovery effect level, evaluate the river recovery effect corresponding to each river section.The present application solves the core problems such as unclear relationship between river recovery target and implementation index, insufficient spatial heterogeneity adaptation and other core problems in the prior art, and provides new technical support for accurate evaluation and scientific measures of river recovery.

Description

Technical Field

[0001] This invention relates to the field of river recovery effectiveness research, specifically to a multi-indicator-based method for evaluating river recovery effectiveness. Background Technology

[0002] Rivers and lakes are the lifeblood of the Earth, nurturing life, supporting development, and carrying civilization. Maintaining the health of rivers and lakes and ensuring their sustainable use is the only way to build a beautiful China and achieve harmonious coexistence between humans and water. Due to limitations in resource endowment, intensified human activities, and global climate change, some regions in my country are experiencing ecological problems such as river flow interruption and lake shrinkage and drying up.

[0003] We have always attached great importance to the health of rivers and lakes. In response to ecological problems such as river flow interruption and lake shrinkage and drying up, we have proposed "starting with the mother rivers in various regions, carrying out the Mother River Restoration Action to get rivers flowing and lakes restored." This comprehensive Mother River Restoration Action includes establishing a database to evaluate its effectiveness, proposing different standards for its implementation, optimizing water resource allocation, restoring good connectivity between rivers and lakes, restoring and improving the water flow in rivers, restoring lake surface area, and repairing damaged river and lake ecosystems. We aim to ensure that each implementation yields tangible results, allowing rivers to regain life and river basins to revitalize. Currently… Various engineering and non-engineering measures have been fully implemented, and the recovery results are gradually becoming apparent. However, due to the vast differences in the hydrological characteristics of the nation's mother rivers, the varying causes of flow interruption among rivers and lakes, and the different focuses of the various measures, large-scale river management and ecological restoration projects have been carried out in major river basins. In the precise management process of the Mother River recovery action, rivers account for approximately 92.5%, necessitating a scientific, comprehensive, and objective evaluation method to quantify the river recovery effectiveness and guide the optimization and precise implementation of subsequent management plans. How to scientifically and rationally evaluate the effectiveness of river recovery actions is a technical challenge hindering the recovery of the river and lake ecological environment.

[0004] The existing river recovery assessment indicator system is incomplete, focusing too much on water quality physicochemical indicators and neglecting the coordinated recovery of hydrological conditions, the integrity of aquatic ecosystems, and social service functions. It cannot fully reflect the systemic characteristics of river recovery across the entire dimensions of "hydrology-water quality-ecology-society". At the same time, it does not distinguish between rigid baseline indicators and flexible improvement indicators, often resulting in inflated assessment results for river sections that fail to meet the baseline requirements such as ecological flow and water quality standards due to the emphasis on a single dimension indicator.

[0005] Existing assessment methods mostly rely on expert scoring, lacking support from objective monitoring data; they fail to consider the differences in hydrology and water quality during different water periods (high / normal / low water periods) and the differences in governance objectives for river sections with different functional orientations, thus failing to adapt to the spatiotemporal heterogeneity of river systems, resulting in significant deviations between assessment results and actual conditions. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for evaluating the effectiveness of river restoration based on multiple indicators, providing standardized and precise technical support for river restoration and management.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A multi-indicator-based method for evaluating the effectiveness of river restoration is provided, comprising the following steps: S1: Construct a five-dimensional evaluation index system for assessing the effectiveness of river restoration, including the stress dimension. State Dimension Elasticity dimension E Response Dimension R Benefits dimension And collect evaluation index data corresponding to each dimension in the five-dimensional evaluation index system. ; S2: Evaluation index data Standardization was performed to obtain standardized indicators, which were then used as observation indicators for river recovery. A measurement model representing the mapping between evaluation indicators and latent variables, a structural model representing the causal relationship between latent variables, and a geographically weighted regression (GWR) model were constructed to quantify the spatial transmission effect of each river segment and calculate the spatial dynamic weights of the observation indicators. S3: Set the level of river recovery effectiveness. k And set the expected value, entropy and hyperentropy of the observation indicators corresponding to each level, and generate the cloud membership matrix corresponding to the recovery level of each indicator; S4: Construct a classical domain matter-element matrix and a section domain matter-element matrix using the observation indicators of each river segment, and calculate the correlation between the observation indicators and the river recovery effectiveness level; S5: Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level by combining correlation degree, membership degree and spatial dynamic weight, and evaluate the river recovery effectiveness corresponding to each river segment.

[0008] Further, step S2 includes: S21: From N Evaluation index data obtained from each river section Standardization is performed to eliminate the dimensions of various types of indicators, resulting in standardized indicators. ; For positive evaluation indicators, the standardization method is as follows: ; For negative evaluation indicators, the standardization method is as follows: ; in, Evaluation indicator data The maximum and minimum values ​​of the indicator data are: a positive indicator indicates that the larger the indicator data value, the better the river recovery effect; a negative indicator indicates that the larger the indicator data value, the worse the river recovery effect. S22: Dimension of Pressure and response dimensions R As an exogenous latent variable, the state dimension Elasticity dimension E and benefit dimensions As an endogenous latent variable; Constructing exogenous latent variable vectors using exogenous latent variables Construct an observation index vector for exogenous latent variables using standardized indices corresponding to the exogenous latent variables. ; Constructing an endogenous latent variable vector using endogenous latent variables Construct an observation index vector of endogenous latent variables using standardized indices corresponding to the endogenous latent variables. ; S23: Construct a measurement model that maps characterization evaluation indicators to latent variables; ; Construct a structural model representing the causal relationship between latent variables; ; in, For the factor loading matrix, For the residual vector, The path coefficient matrix among endogenous latent variables. This is the path coefficient matrix of exogenous latent variables to endogenous latent variables; S24: Use the standardized indicators corresponding to each evaluation indicator data as observation indicators, and use the observation indicators corresponding to each river segment as a sample. Each sample contains... P Each observation indicator forms N For each sample, calculate the sample covariance to obtain the sample covariance matrix. S ; ; in, n For sample number, For the first n The observation vector of each sample, This is the sample mean vector; S25: Construct the implicit covariance matrices of the measurement and structural models. ; ; in, The covariance matrix among the exogenous latent variables. Here is the error covariance matrix of the observed indicators corresponding to the exogenous latent variables; error covariance matrix The diagonal elements in the table represent the measurement error variance of the observed indicators, and the off-diagonal elements represent the measurement error covariance between the observed indicators. S26: Based on the latent covariance matrix Construct the objective function of the maximum likelihood fitting function; ; in, The maximum likelihood fitting function value, Let be the trace function of the matrix; S27: Based on the objective function in the sample covariance matrix S The above iterative solution is performed to obtain the factor loadings of each observed indicator in the sample for exogenous and endogenous latent variables. Calculate the basic causal weights of each observed indicator. ; ; S28: Construct a geographically weighted regression (GWR) model to quantify the spatial transfer effects of different river segments, utilizing observational indicators corresponding to different river segments. Calculate the spatial regression coefficients of the observed indicators in each river section; ; in, For the first n The overall recovery score of each river section was used as an explanatory variable. The geographical coordinates of the center point of the river section For spatial intercept term, For random error term, p The number of the observed indicator, For the first p The observation index at the first n Spatial regression coefficients for each river segment; S29: Calculate the spatial correction coefficient based on the spatial regression coefficient. ; ; S210: Utilizing spatial correction coefficients and basic causal weights Calculate the first n The first section of the river p Spatial dynamic weights of each observation index ; .

[0009] Further, step S3 includes: S31: Set the level of river recovery effectiveness. kand set each level k Expected value of the corresponding observation index ,entropy and hyperentropy ; ; in, The upper and lower limits of the observation indicators for assessing the effectiveness of river recovery are set. K The number of levels representing the effectiveness of river recovery; S32: In terms of entropy The expected matrix and hyperentropy of the observed indicators Generate normal random numbers for the standard deviation matrix of the observed indicators. ; ; in, It is a norm function; S33: Calculate the membership degree of the observed indicators corresponding to the river recovery effectiveness level. , get about K Cloud membership matrix for river recovery effectiveness levels ; ; in, These are the observed indicators in the sample.

[0010] Further, step S4 includes: S41: Construct a classical domain matter-element matrix using observation indicators for each river segment. and the domain matter matrix ; ; in, For the range of sections, For the first p The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. = , For the first P The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. For the first p The threshold range of each indicator. For the first P The threshold range of each indicator; S42: Based on the Classical Domain Element Matrix and the domain matter matrix Calculate the correlation between observed indicators and the level of river recovery effectiveness. ; ; in, For the first p Observation indicators Corresponding to the k The correlation between the effectiveness levels of river recovery and other factors. For observation indicators to the observation index threshold range The distance, For observation indicators To the segment threshold range The distance.

[0011] Further, step S5 includes: S51: Combining spatial dynamic weights Membership degree and Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level. ; ; S52: Obtain the comprehensive correlation data of the river recovery effectiveness level for each river segment, and select the maximum value. , to the maximum value The corresponding river recovery effectiveness level is used as the final assessment of river recovery effectiveness.

[0012] The beneficial effects of this invention are as follows: This invention constructs a five-dimensional causal multi-indicator system of pressure-state-elasticity-response-benefit, introducing a differentiated threshold for ecological baselines to address the shortcomings of uniform standards in adapting to natural endowments. It quantifies the causal path contributions and upstream-downstream spatial transmission effects of multiple driving factors through a geographically weighted regression-structural equation model (GWR-SEM), overcoming the limitations of traditional static weights in characterizing spatial interactions and causal logic. Simultaneously, it constructs a cloud model-matter-extension coupling assessment model to synchronously handle the fuzziness and randomness of the assessment process, accurately quantifying the comprehensive level of river recovery. This invention solves the core problems of existing technologies, such as unclear relationships between river recovery goals and implementation indicators, and insufficient adaptation to spatial heterogeneity, providing new technical support for accurate assessment and scientific policy implementation in river recovery. Attached Figure Description

[0013] Figure 1 This is a flowchart of a multi-indicator-based method for evaluating the effectiveness of river restoration. Detailed Implementation

[0014] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0015] like Figure 1 As shown, a multi-indicator-based method for evaluating the effectiveness of river restoration includes the following steps: S1: Construct a five-dimensional evaluation index system for assessing the effectiveness of river restoration, including the stress dimension. State Dimension Elasticity dimension E Response Dimension R Benefits dimension And collect evaluation index data corresponding to each dimension in the five-dimensional evaluation index system. , i As a dimension, j The evaluation indicator data is numbered; For example, taking the Henan River, known as the "Mother River," as the object of restoration, the process of constructing a five-dimensional evaluation index system and collecting evaluation index data requires understanding the restoration goals, the tasks and measures of restoration actions, and the types of rivers and lakes.

[0016] The Nanchuan River is a right-bank tributary of the Huangshui River, a first-level tributary of the Yellow River. Its main stream flows from southwest to northeast, passing through Zongzhai Township and entering Xining City northeast of Lujiazhai. It joins the Huangshui River at the Huangshui Bridge on Changjiang Road in the city center, and is considered the mother river of Xining. From its source to Shangxinzhuang Mingmajigou in Huangzhong County, the main stream is called the Nanchuan River, with a length of 49.0 km and a drainage area of ​​398 km². The restoration project mainly covers the main stream of the Nanchuan River and the Mendan Gorge River, with a total length of 71.53 km.

[0017] In response to the current situation and existing problems in the Nanchuan River Basin, the project will deploy limiting facilities to ensure the discharge of the ecological base flow of the Nanchuan River, carry out engineering measures to improve the space on both sides of the river, promote ecological environment restoration, enhance soil and water conservation, and consolidate flood control capabilities.

[0018] Based on the existing restoration measures of the Mother River, a five-dimensional evaluation index system will be constructed, and evaluation index data of each dimension will be collected to assess the restoration effect. Specifically, this includes: Stress dimension External driving constraints for river recovery ， Stress dimension The corresponding evaluation indicators include climate change pressure and human activity pressure. Climate change pressure includes the annual precipitation variation coefficient and the frequency of extreme precipitation, while human activity pressure includes the proportion of watershed construction land and the intensity of agricultural non-point source pollution load. State Dimension The core status indicator for river recovery is the state dimension. The corresponding evaluation index data include hydrological situation status, water quality physicochemical status and aquatic ecological structure status. Hydrological situation status includes ecological flow compliance rate and ecological water volume / ecological water level. Water quality physicochemical status includes water quality status of major cross sections. Aquatic ecological structure status includes fish integrity index, benthic animal integrity index and riparian vegetation coverage. Elasticity dimension E As an indicator of the resilience of river recovery, the resilience dimension E The corresponding evaluation indicators include ecological flow guarantee indicators, connectivity restoration indicators, groundwater over-extraction control indicators, water environment improvement indicators, and water ecological restoration indicators. Ecological flow guarantee indicators mainly include the compliance rate of ecological water volume (flow) at control sections; connectivity restoration indicators include river length with water, duration of water flow, and number and duration of full-line connection; groundwater over-extraction control includes groundwater recovery around the river; water environment improvement indicators include water quality data of restored river sections and ecological water volume / water level change data of important wetlands in nature reserves; water ecological restoration indicators include river length for maintaining cleanliness and unobstructed flow, ecological water level change, comprehensive assessment indicators for biodiversity restoration, and fish population index, etc. Response Dimension R Indicators for river restoration management measures, response dimensions R The corresponding evaluation index data include engineering governance response and management and control response. Engineering governance response includes the proportion of the area of ​​engineering measures for restoration and governance to the river basin area. Engineering measures include the construction of ecological flow release facilities for controlling water conservancy and hydropower projects, river regulation and water system connectivity projects, irrigation area water-saving renovation, ecological water replenishment measures, groundwater over-extraction control projects, and monitoring capacity building projects. Management and control response includes the density of water environment monitoring network. Management measures mainly include the completion status of river restoration targets, initial water rights allocation, ecological flow verification and guarantee, total water intake and water withdrawal control and water withdrawal permits, hydrological monitoring and analysis management, etc. Benefit Dimension As a comprehensive output indicator for river restoration, the benefit dimension The corresponding evaluation indicators include ecological benefits and economic benefits. Ecological benefits include the increase in the value of ecosystem services, while economic benefits include the increase in the output value of ecotourism and the increase in irrigation water supply security.

[0019] S2: Evaluation index data Standardization was performed to obtain standardized indicators, which were then used as observation indicators for river recovery. A measurement model representing the mapping between evaluation indicators and latent variables, a structural model representing the causal relationship between latent variables, and a geographically weighted regression (GWR) model were constructed to quantify the spatial transmission effect of each river segment and calculate the spatial dynamic weights of the observation indicators.

[0020] This embodiment is illustrated by... Step S2 specifically includes: S21: From N Evaluation index data obtained from each river section Standardization is performed to eliminate the dimensions of various types of indicators, resulting in standardized indicators. ; For positive evaluation indicators, the standardization method is as follows: ; For negative evaluation indicators, the standardization method is as follows: ; in, Evaluation indicator data The maximum and minimum values ​​of the indicator data are: a positive indicator indicates that the larger the indicator data value, the better the river recovery effect; a negative indicator indicates that the larger the indicator data value, the worse the river recovery effect. S22: Dimension of Pressure and response dimensions R As an exogenous latent variable, the state dimension Elasticity dimension E and benefit dimensions As an endogenous latent variable; Constructing exogenous latent variable vectors using exogenous latent variables Construct an observation index vector for exogenous latent variables using standardized indices corresponding to the exogenous latent variables. ; Constructing an endogenous latent variable vector using endogenous latent variables Construct an observation index vector of endogenous latent variables using standardized indices corresponding to the endogenous latent variables. ; S23: Construct a measurement model that maps characterization evaluation indicators to latent variables; ; Construct a structural model representing the causal relationship between latent variables; ; in, For the factor loading matrix, For the residual vector, The path coefficient matrix among endogenous latent variables. This is the path coefficient matrix of exogenous latent variables to endogenous latent variables; Exogenous latent variable vector Exogenous latent variables are packaged into a set of variables; similarly, endogenous latent variable vectors are formed. Endogenous latent variables are packaged, while observed index vectors are... Observation index vector The standardized metrics, normalized according to the corresponding dimensions, are packaged. In this embodiment, during the construction of each matrix, missing elements are set to 0 to ensure alignment of the matrix dimensions.

[0021] In the structural model, the vector of endogenous latent variables on the left side of the equation As the "result being explained", it represents the state dimension that will ultimately be fully explained. Elasticity dimension E and benefit dimensions The vector of endogenous latent variables on the right side of the equation As "the reason why endogenous latent variables explain each other", it expresses the mutual causal relationship between endogenous latent variables.

[0022] S24: Use the standardized indicators corresponding to each evaluation indicator data as observation indicators, and use the observation indicators corresponding to each river segment as a sample. Each sample contains... P Each observation indicator forms N For each sample, calculate the sample covariance to obtain the sample covariance matrix. S ; ; in, n For sample number, For the first n The observation vector of each sample, This is the sample mean vector; S25: Construct the implicit covariance matrices of the measurement and structural models. ; ; in, The covariance matrix among the exogenous latent variables. Here is the error covariance matrix of the observed indicators corresponding to the exogenous latent variables; error covariance matrix The diagonal elements in the table represent the measurement error variance of the observed indicators, and the off-diagonal elements represent the measurement error covariance between the observed indicators. S26: Based on the latent covariance matrix Construct the objective function of the maximum likelihood fitting function; ; in, The maximum likelihood fitting function value, Let be the trace function of the matrix; S27: Based on the objective function in the sample covariance matrix S The above iterative solution is performed to obtain the factor loadings of each observed indicator in the sample for exogenous and endogenous latent variables. Calculate the basic causal weights of each observed indicator. ; ; Basic causal weights It is used to characterize the explanatory power of the corresponding observation indicators for latent variables.

[0023] S28: Construct a geographically weighted regression (GWR) model to quantify the spatial transfer effects of different river segments, utilizing observational indicators corresponding to different river segments. Calculate the spatial regression coefficients of the observed indicators in each river section; ; in, For the first n The overall recovery score of each river section was used as an explanatory variable. The geographical coordinates of the center point of the river section For spatial intercept term, For random error term, p The number of the observed indicator, For the first p The observation index at the first n Spatial regression coefficients for each river segment; S29: Calculate the spatial correction coefficient based on the spatial regression coefficient. ; ; S210: Utilizing spatial correction coefficients and basic causal weights Calculate the first n The first section of the river p Spatial dynamic weights of each observation index ; .

[0024] S3: Set the level of river recovery effectiveness. k Then, the expected value, entropy, and hyperentropy of the observation indicators corresponding to each level are set, and the cloud membership matrix corresponding to the recovery level of each indicator is generated. Step S3 specifically includes: S31: Set the level of river recovery effectiveness. k and set each level k Expected value of the corresponding observation index ,entropy and hyperentropy ; ; in, The upper and lower limits of the observation indicators for assessing the effectiveness of river recovery are set. K The river recovery effectiveness is categorized into five levels: extremely poor, poor, average, good, and excellent. K =5; S32: In terms of entropy The expected matrix and hyperentropy of the observed indicators Generate normal random numbers for the standard deviation matrix of the observed indicators. ; ; in, It is a norm function; S33: Calculate the membership degree of the observed indicators corresponding to the river recovery effectiveness level. , get about K Cloud membership matrix for river recovery effectiveness levels ; ; in, These are the observed indicators in the sample.

[0025] This invention obtains the causal basis weights of observed indicators through structural equation modeling, and captures the spatial effects of upstream and downstream rivers through geographically weighted regression, forming a dynamic weighting system that combines causality and spatial heterogeneity.

[0026] S4: Construct a classical domain matter-element matrix and a section domain matter-element matrix using the observation indicators for each river segment, and calculate the correlation between the observation indicators and the river recovery effectiveness level. Step S4 specifically includes the following steps: S41: Construct a classical domain matter-element matrix using observation indicators for each river segment. and the domain matter matrix ; ; in, For the range of sections, For the first p The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. = , For the first P The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. For the first pThe threshold range for each indicator is determined by the maximum and minimum values ​​of the river recovery effectiveness level indicator. For the first P The threshold range of each indicator; S42: Based on the Classical Domain Element Matrix and the domain matter matrix Calculate the correlation between observed indicators and the level of river recovery effectiveness. ; ; in, For the first p Observation indicators Corresponding to the k The correlation between the effectiveness levels of river recovery and other factors. For observation indicators to the observation index threshold range The distance, For observation indicators To the segment threshold range The distance; Observation indicators to the observation index threshold range distance The calculation formula is: .

[0027] Observation indicators To the segment threshold range distance The calculation method is the same.

[0028] S5: Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level by combining correlation, membership, and spatial dynamic weights, and evaluate the river recovery effectiveness corresponding to each river segment. Step S5 specifically includes the following steps: S51: Combining spatial dynamic weights Membership degree and Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level. ; ; S52: Obtain the comprehensive correlation data of the river recovery effectiveness level for each river segment, and select the maximum value. , to the maximum value The corresponding river recovery effectiveness level is used as the final assessment of river recovery effectiveness.

[0029] This invention constructs a five-dimensional causal multi-indicator system encompassing pressure, state, resilience, response, and benefit. It introduces differentiated thresholds for ecological baselines to address the shortcomings of uniform standards in adapting to natural endowments. A geographically weighted regression-structural equation model (GWR-SEM) quantifies the causal path contributions and upstream-downstream spatial transmission effects of multiple driving factors, overcoming the limitations of traditional static weights in characterizing spatial interactions and causal logic. Simultaneously, a cloud model-matter-extension coupling assessment model is constructed to address the fuzziness and randomness of the assessment process, accurately quantifying the comprehensive level of river recovery.

[0030] This invention constructs an evaluation index system for the effectiveness of the Mother River restoration action based on restoration goals and the implementation indicators of tasks and measures. It comprehensively proposes various restoration goals around the three principles of "water resources, water environment, and water ecology," while also considering both engineering and non-engineering measures to propose implementation indicators for various tasks and measures. Therefore, this index system is results-oriented, considering the restoration effect of the Mother River; and it strengthens process management, emphasizing the necessity of each task and measure.

[0031] This invention solves the core problems of existing technologies, such as unclear relationships between river restoration goals and implementation indicators and insufficient adaptation to spatial heterogeneity, and provides new technical support for accurate assessment and scientific policy implementation in river restoration.

Claims

1. A method for evaluating the effectiveness of river restoration based on multiple indicators, characterized in that, Includes the following steps: S1: Construct a five-dimensional evaluation index system for assessing the effectiveness of river restoration, including the stress dimension. State dimension Elasticity dimension E Response Dimension R Benefits dimension ; And collect evaluation index data corresponding to each dimension in the five-dimensional evaluation index system. ; S2: Evaluation index data Standardization was performed to obtain standardized indicators, which were then used as observation indicators for river recovery. A measurement model representing the mapping between evaluation indicators and latent variables, a structural model representing the causal relationship between latent variables, and a geographically weighted regression (GWR) model were constructed to quantify the spatial transmission effect of each river segment and calculate the spatial dynamic weights of the observation indicators. S3: Set the level of river recovery effectiveness. k And set the expected value, entropy and hyperentropy of the observation indicators corresponding to each level, and generate the cloud membership matrix corresponding to the recovery level of each indicator; S4: Construct a classical domain matter-element matrix and a section domain matter-element matrix using the observation indicators of each river segment, and calculate the correlation between the observation indicators and the river recovery effectiveness level; S5: Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level by combining correlation degree, membership degree and spatial dynamic weight, and evaluate the river recovery effectiveness corresponding to each river segment.

2. The method for evaluating the effectiveness of river restoration based on multiple indicators according to claim 1, characterized in that, Step S2 includes: S21: From N Evaluation index data obtained from each river section Standardization is performed to eliminate the dimensions of various types of indicators, resulting in standardized indicators. ; For positive evaluation indicators, the standardization method is as follows: ； For negative evaluation indicators, the standardization method is as follows: ； in, Evaluation indicator data The maximum and minimum values ​​of the indicator data are: a positive indicator means that the larger the indicator data value, the better the river recovery effect; a negative indicator means that the larger the indicator data value, the worse the river recovery effect. S22: Dimension of Pressure and response dimensions R As an exogenous latent variable, the state dimension Elasticity dimension E and benefit dimensions As an endogenous latent variable; Constructing exogenous latent variable vectors using exogenous latent variables Construct an observation index vector for exogenous latent variables using standardized indices corresponding to the exogenous latent variables. ; Constructing an endogenous latent variable vector using endogenous latent variables Construct an observation index vector of endogenous latent variables using standardized indices corresponding to the endogenous latent variables. ; S23: Construct a measurement model that maps characterization evaluation indicators to latent variables; ； Construct a structural model representing the causal relationship between latent variables; ； in, For the factor loading matrix, For the residual vector, The path coefficient matrix among endogenous latent variables. This is the path coefficient matrix of exogenous latent variables to endogenous latent variables; S24: Use the standardized indicators corresponding to each evaluation indicator data as observation indicators, and use the observation indicators corresponding to each river segment as a sample. Each sample contains... P Each observation indicator forms N For each sample, calculate the sample covariance to obtain the sample covariance matrix. S ; ； in, n For sample number, For the first n The observation vector of each sample, This is the sample mean vector; S25: Construct the implicit covariance matrices of the measurement and structural models. ; ； in, The covariance matrix among the exogenous latent variables. Here is the error covariance matrix of the observed indicators corresponding to the exogenous latent variables; error covariance matrix The diagonal elements in the table represent the measurement error variance of the observed indicators, and the off-diagonal elements represent the measurement error covariance between the observed indicators. S26: Based on the latent covariance matrix Construct the objective function of the maximum likelihood fitting function; ； in, The maximum likelihood fitting function value, Let be the trace function of the matrix; S27: Based on the objective function in the sample covariance matrix S The above iterative solution is performed to obtain the factor loadings of each observed indicator in the sample for exogenous and endogenous latent variables. Calculate the basic causal weights of each observed indicator. ; ； S28: Construct a geographically weighted regression (GWR) model to quantify the spatial transfer effects of different river segments, utilizing observational indicators corresponding to different river segments. Calculate the spatial regression coefficients of the observed indicators in each river section; ； in, For the first n The overall recovery score of each river section was used as an explanatory variable. The geographical coordinates of the center point of the river section For spatial intercept term, For random error term, p The number of the observed indicator, For the first p The observation index in the first n Spatial regression coefficients for each river segment; S29: Calculate the spatial correction coefficient based on the spatial regression coefficient. ; ； S210: Utilizing spatial correction coefficients and basic causal weights Calculate the first n The first section of the river p Spatial dynamic weights of each observation index ; 。 3. The method for evaluating the effectiveness of river restoration based on multiple indicators according to claim 2, characterized in that, Step S3 includes: S31: Set the level of river recovery effectiveness. k and set each level k Expected value of the corresponding observation index ,entropy and hyperentropy ; ； in, The upper and lower limits of the observation indicators for assessing the effectiveness of river recovery are set. K The number of levels representing the effectiveness of river recovery; S32: with entropy The expected matrix and hyperentropy of the observed indicators Generate normal random numbers for the standard deviation matrix of the observed indicators. ; ； in, It is a norm function; S33: Calculate the membership degree of the observed indicators corresponding to the river recovery effectiveness level. , get about K Cloud membership matrix for river recovery effectiveness levels ; ； in, These are the observed indicators in the sample.

4. The method for evaluating the effectiveness of river recovery based on multiple indicators according to claim 3, characterized in that, Step S4 includes: S41: Construct a classical domain matter-element matrix using observation indicators for each river segment. and the domain matter matrix ; ； in, For the range of sections, For the first p The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. = , For the first P The first indicator corresponds to the first k The threshold range of observation indicators for the effectiveness level of river recovery. For the first p The threshold range of each indicator. For the first P The threshold range of each indicator; S42: Based on the Classical Domain Element Matrix and the domain matter matrix Calculate the correlation between observed indicators and the level of river recovery effectiveness. ; ； in, For the first p Observation indicators Corresponding to the k The correlation between the effectiveness levels of river recovery and other factors. For observation indicators to the observation index threshold range The distance, For observation indicators To the segment threshold range The distance.

5. The method for evaluating the effectiveness of river recovery based on multiple indicators according to claim 4, characterized in that, Step S5 includes: S51: Combining spatial dynamic weights Membership degree and Calculate the comprehensive correlation between each river segment and the river recovery effectiveness level. ; ； S52: Obtain the comprehensive correlation data of the river recovery effectiveness level for each river segment, and select the maximum value. , to the maximum value The corresponding river recovery effectiveness level is used as the final assessment of river recovery effectiveness.

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