CN116167549A - Water resource bearing capacity evaluation method - Google Patents
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Abstract
The invention provides a water resource bearing capacity evaluation method, and belongs to the technical field of water resource bearing capacity evaluation. According to the method, indexes appearing in the designated places are collected, an evaluation index system is constructed based on the collection condition of target water resource area index data, the index system is divided into different subsystem indexes according to index characteristics, then entropy weight main components are subjected to dimension reduction processing on each subsystem index, finally the target water resource bearing capacity is evaluated based on the evaluation index coupling TOPSIS method.
Description
Technical Field
The invention belongs to the technical field of water resource bearing capacity evaluation, and particularly relates to a water resource bearing capacity evaluation method.
Background
The water resource bearing capacity is a branch of the resource environment bearing capacity refined based on the constituent elements. The water resource is used as an important component of resource environment and is closely related to social and economic development and people's life. The water resource is taken as a host of the water resource bearing capacity, and the object is a human and living socioeconomic system and an ecological system. The water resource bearing capacity is to meet the demand and pressure of objects on a subject, define the water consumption threshold of a socioeconomic and ecological environment system in a water resource system, and judge whether the influence of human activities on the water resource system is excessive. Therefore, it is necessary to develop water resource bearing capacity evaluation research to evaluate whether the water resource usage exceeds the bearing capacity range, so that an optimization strategy can be provided in a targeted manner, technical support is provided for the later water environment condition guarantee and improvement, and the method has very important practical significance for building a healthy sustainable development type society.
The existing water resource bearing capacity evaluation method mainly starts with phenomenon analysis in a water resource bearing capacity system to construct an index system, starts with qualitative analysis from the phenomenon, ignores the interrelationship among all factors in the water resource bearing capacity system, is greatly influenced by the subjectivity of researchers, and leads to lower accuracy of the finally obtained water resource bearing capacity.
Disclosure of Invention
Aiming at the defects in the prior art, the water resource bearing capacity evaluation method provided by the invention solves the problem of low accuracy of the water resource bearing capacity obtained based on the existing evaluation method.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the scheme provides a water resource bearing capacity evaluation method, which comprises the following steps:
s1, constructing an evaluation index system, and dividing the evaluation index system into different subsystem indexes according to index characteristics;
s2, performing entropy weight principal component dimension reduction processing on each subsystem index, and constructing an entropy weight-principal component analysis model;
and S3, evaluating the bearing capacity of the target water resource by using a coupling TOPSIS method based on the entropy weight-principal component analysis model.
The beneficial effects of the invention are as follows: according to the method, indexes appearing in the designated places are collected, an evaluation index system is constructed based on the collection condition of target water resource area index data, the index system is divided into different subsystem indexes according to index characteristics, then entropy weight main components are subjected to dimension reduction processing on each subsystem index, finally the target water resource bearing capacity is evaluated based on the evaluation index coupling TOPSIS method.
Further, the step S1 includes the steps of:
s101, collecting water resource area indexes in a designated area;
s102, constructing an evaluation index system based on the water resource area index, and dividing the evaluation index system into different subsystem indexes according to index characteristics.
The beneficial effects of the above-mentioned further scheme are: according to the method, the characteristics of water resource distribution in the designated area are collected, the high-frequency indexes are deleted based on the collection condition of index data of the target water resource area to form an initial index library, then indexes in the initial index library are divided into different subsystem indexes according to index characteristics, the sustainability of the water resources in the river basin is comprehensively reflected, and a theoretical basis is provided for evaluating the water resource bearing capacity under the river basin scale.
Still further, the step S2 includes the steps of:
s201, carrying out standardization processing on each subsystem index by utilizing an entropy weight principal component analysis method to obtain a score value of an evaluation index, and carrying out dimension reduction calculation based on the score value, wherein the number of principal component factors is more than 5;
s202, based on the dimension reduction result, checking whether the index data passes the significance test, if so, entering a step S203, otherwise, returning to the step S201;
s203, determining the contribution degree of each principal component factor according to the judging result, loading entropy information and covariance information by a coupling entropy weight method, and constructing an entropy weight-principal component analysis model.
The beneficial effects of the above-mentioned further scheme are: by constructing the entropy weight-principal component analysis model, the purposes of retaining important information and reducing redundancy by utilizing entropy difference can be achieved, the objectivity and fairness of the evaluation result are improved, and the error effect of the evaluation result caused by subjectivity of the traditional principal component analysis is made up.
Still further, the normalization process in step S201 is as follows:
if each subsystem index is a forward index, the score value of the evaluation index is obtained according to the following formula:
if each subsystem index is a negative index, the score value of the evaluation index is obtained according to the following formula:
wherein ,Vij The score of the ith evaluation object under the jth index in the evaluation index matrix V is represented as V ij >10 is taken as V ij When V is the value of ij <When 0, 0 is taken as V ij >10 value, min i (x ij ) Represents the lower limit value, max, of the category standard of the ith evaluation object under the jth index in the evaluation index matrix V i (x ij ) Represents the upper limit value, x of the class standard of the ith evaluation object under the jth index in the evaluation index matrix V ij The original number of the ith evaluation object under the jth index in the evaluation index matrix V is represented.
The beneficial effects of the above-mentioned further scheme are: because the evaluation indexes have different sizes and magnitudes and different properties, the original index value is directly used for analysis, the effect of the index with higher value in comprehensive analysis can be highlighted, the effect of the index with lower value level is relatively weakened, the original index data is standardized, and the reliability of the result is ensured.
Still further, the expression of the entropy weight-principal component analysis model in step S203 is as follows:
h j ＝1-E j
Y＝[y 1 ,y 2 ,...,y r ]
wherein ,Ej Entropy of the j index is represented, and E is 0-E j Less than or equal to 1, n represents the number of evaluation indexes, m represents the number of evaluation objects, h j Representing the number difference coefficient, W j Weight, W representing any index of the ith evaluation object ij The weight of the j index in the i-th evaluation object is represented,
represents a matrix of j x r,/j>
Represents a matrix of j x j, Y represents a matrix of r x r, r represents the number of principal component factors,/->
Representation->
The point vector of the j-th index of (b), y r Point vector representing the r-th principal component factor in Y,/->
A score indicating the kth principal component factor in the ith evaluation object.
The beneficial effects of the above-mentioned further scheme are: the entropy weight method can process different data matrixes, including raster image matrixes generated by different indexes, so as to achieve the purposes of retaining important information and reducing redundancy by utilizing entropy differences. The entropy weight method is combined with the principal component analysis method, and entropy information and covariance information are loaded, so that a better identification effect is achieved.
Still further, the step S3 includes the steps of:
s301, determining positive and negative ideal solutions and distances and proximity of each evaluation object to the ideal solutions through a TOPSIS method according to an entropy weight-principal component analysis model and an evaluation index system;
s302, constructing a coupling entropy weight main component-TOPSIS model according to positive and negative ideal solutions and the distance and the closeness between each evaluation object and the ideal solution;
s303, evaluating the bearing capacity of the target water resource by utilizing an entropy weight main component-TOPSIS model.
The beneficial effects of the above-mentioned further scheme are: the sorting is carried out according to the proximity degree of a limited number of evaluation objects and an idealized target by the TOPSIS method, the original data information is fully utilized, the sorting result can quantitatively reflect the quality degree of different evaluation objects, the sorting method is visual and reliable, the influence caused by different dimension can be eliminated, and the data deformation generated in the dimension reduction processing process of the entropy weight-principal component model can be compensated.
Still further, the expressions of the distance and the proximity of the positive and negative ideal solutions to the ideal solutions for each evaluation object are as follows:
wherein ,X+ Representing the positive ideal solution, X - Representing a negative ideal solution to the problem of,
positive ideal meaning of the nth index, < +.>
Negative ideal solution representing the nth index, < ->
Representing the positive ideal solution distance, +.>
The negative ideal solution distance is represented, n represents the number of indexes, j=1, 2,.. i Representing ideal solution proximity.
The beneficial effects of the above-mentioned further scheme are: the subjectivity of the data is avoided by the method for calculating the distance and the proximity between the evaluation object and the ideal solution, the objective function is not needed, the inspection is not needed, and the comprehensive influence degree of a plurality of influence indexes can be better depicted.
Still further, the expression of the entropy weight principal component-TOPSIS model is as follows:
wherein ,
represents the final overall evaluation score of the i-th evaluation object, b represents the coefficient, < ->
Score representing kth principal component factor in ith evaluation object,/for>
The entropy weight of the kth principal component factor in the ith evaluation object is represented as a composite score of the principal component factor score, and r represents the number of principal component factors.
The beneficial effects of the above-mentioned further scheme are: the entropy weight principal component-TOPSIS model combines the advantages of entropy weight-principal component analysis and TOPSIS method, and can effectively eliminate partial errors caused by human factors. The method has the advantages of global and local advantages, and can obtain the water resource bearing capacity evaluation result with higher spatial resolution on the drainage basin scale.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
Examples
As shown in fig. 1, the invention provides a water resource bearing capacity evaluation method, which comprises the following steps:
s1, constructing an evaluation index system, dividing the evaluation index system into different subsystem indexes according to index characteristics, and realizing the method as follows:
s101, collecting water resource area indexes in a designated area;
s102, constructing an evaluation index system based on the water resource area index, and dividing the evaluation index system into different subsystem indexes according to index characteristics.
In this embodiment, an evaluation index system is constructed based on indexes collected from documents and issued from official websites, and is divided into different subsystem indexes according to index features. Wherein the evaluation index system comprises a hydrological subsystem, a socioeconomic subsystem, an ecological environment subsystem and a hydraulic engineering subsystem, each subsystem comprises a plurality of evaluation indexes, and a multi-objective decision matrix X { (X) is established based on the evaluation system ij ) m×n I=1, 2, m; j=1, 2, & gt, n }. In the matrix, m is the number of evaluation objects, n is the number of evaluation indexes, and x ij The i-th evaluation target value under the j-th index.
In this embodiment, the hydrological subsystem includes precipitation C v And the evapotranspiration C v' . The socioeconomic subsystem comprises a water consumption per person, a water consumption per GDP, a water consumption per agricultural area, a water consumption per industrial increase, a town ratio and a proportion of the first industry. The ecological environment subsystem comprises a water production coefficient, a water source conservation amount, a net ecological system productivity, a water area ratio, ecological integrity, ecological diversity, a non-point source pollution amount, a soil erosion amount, an ecological water demand satisfaction degree, an underground water level reduction rate, an effective irrigation rate and a water-soil conservation treatment area ratio. The hydraulic engineering subsystem comprises a reservoir capacity change rate and a local water supply duty ratio.
In this example, referring to table 1, table 1 is an evaluation index system table in which the water resource load bearing capacity is the target layer. The hydrological subsystem, the socioeconomic subsystem, the ecological environment subsystem and the hydraulic engineering subsystem are taken as a criterion layer.In precipitation amount C v Amount of transpiration C v' The water consumption per unit, the water consumption per unit GDP, the agricultural water consumption per unit area, the increased water consumption per unit industry, the town ratio, the proportion of the first industry, the water yield coefficient, the water source conservation quantity, the net ecological system productivity, the water area occupation ratio, the ecological integrity, the ecological diversity, the non-point source pollution quantity, the soil erosion quantity, the ecological water demand satisfaction degree, the groundwater level dropping rate, the effective irrigation rate, the water-soil conservation treatment area ratio, the reservoir capacity change rate and the local water supply occupation ratio are used as index layers. It can be seen that the target layer for the water resource bearing capacity comprises a plurality of criterion layers, and each criterion layer comprises a plurality of index layers.
TABLE 1
The evaluation index system of table 1 is determined, and the historical actual values or predicted values of each evaluation index in the evaluation area at a certain time in the future are obtained.
S2, performing entropy weight principal component dimension reduction processing on each subsystem index, and constructing an entropy weight-principal component analysis model, wherein the implementation method is as follows:
s201, carrying out standardization processing on each subsystem index by utilizing an entropy weight principal component analysis method to obtain a score value of an evaluation index, and carrying out dimension reduction calculation based on the score value, wherein the number of principal component factors is more than 5;
s202, based on the dimension reduction result, checking whether the index data passes the significance test, if so, entering a step S203, otherwise, returning to the step S201;
s203, determining the contribution degree of each principal component factor according to the judging result, loading entropy information and covariance information by a coupling entropy weight method, and constructing an entropy weight-principal component analysis model.
In the embodiment, performing entropy weight main component dimension reduction processing on each subsystem index; the evaluation index system comprises a hydrological subsystem, a socioeconomic subsystem, an ecological environment subsystem and a hydraulic engineering subsystem, wherein each subsystem comprises a plurality of evaluation indexes.
In the embodiment, the dimension reduction calculation is performed on the standardized indexes of each subsystem by a principal component analysis method, and the number of principal component factors is more than 5; the Bartlett sphericity test and the Kaiser-Meyer-Olkin (KMO) test index data pass through the significance test, if yes, the next step is executed, and if not, the dimension reduction process is executed again until the significance test is passed; and determining the contribution degree of each principal component factor, loading entropy information and covariance information by a coupling entropy weight method, and establishing a coupling entropy weight-principal component analysis model.
Specifically, there are m evaluation targets, n evaluation indexes, x ij A multi-objective decision matrix x= (X) formed by the value of the ith evaluation object under the jth index ij ) m×n 。
For example, the original evaluation matrix established for the water resource bearing capacity of the evaluation area for the past 20 years according to the evaluation index system (22 indexes) of table 1 is x= (X) ij ) 20×22 。
Multiple objective decision matrix X= (X) of each evaluation index raw data ij ) m×n Is normalized to obtain a matrix v= (V ij ) m×n And the comprehensive score values of the hydrological subsystem, the socioeconomic subsystem, the ecological environment subsystem and the hydraulic engineering subsystem are obtained by calculating the coupling entropy weight main component factor method according to the following principles and standards.
Principal component analysis is a multivariate statistical method of multivariate decremental analysis. The method avoids information overlapping and covering caused by high correlation among indexes in dimension reduction calculation, reduces the variable quantity, reduces the calculation time and improves the calculation efficiency; this ensures relatively complete and complete data information and improves the accuracy of the evaluation.
In this embodiment, the dimension reduction processing and the principal component analysis are performed on the data in the evaluation index system, and specifically include the following steps:
original data matrix x= (X) of evaluation index by using SPSS 22.0 pair ij ) m×n The Bartlett sphericity test and Kaiser-Meyer-Olkin (KMO) test are carried out to calculate lambda of the correlation coefficient matrix m Eigenvalue and i-th evaluation object lambda i Characteristic values.
Specifically, the Bartlett's sphere test is used for testing the correlation among variables in a correlation matrix, and whether the variables are unit matrixes or not, namely, whether the variables are independent or not is tested; the KMO (Kaiser-Meyer-Olkin) test statistic is an index for comparing simple correlation coefficients and partial correlation coefficients between variables, when the square sum of the simple correlation coefficients between all the variables is far greater than the square sum of the partial correlation coefficients, the closer the KMO value is to 1, meaning that the stronger the correlation between the variables is, the more suitable the original variables are for factor analysis; when the sum of squares of simple correlation coefficients between all variables is close to 0, the closer the KMO value is to 0, meaning that the weaker the correlation between variables, the less suitable the original variables are for factor analysis.
In this embodiment, the principal component analysis is specifically represented by the following formula:
according to the eigenvalue lambda m and λi Determining a factor number gamma, selecting a characteristic value larger than 1, determining a principal component factor with the characteristic value larger than 1 as a screening condition, and calculating the contribution of the ith principal component factor:
calculating cumulative contribution gamma of the r-th principal component factor r ：
wherein ,γi Represents the contribution of the ith evaluation object, gamma r The cumulative contribution degree of the r-th principal component factor is represented, the contribution degree represents the capability of each principal component factor to represent the original index system information, and the higher the contribution degree is, the stronger the reducing capability of the principal component factor to the original index system information is, and lambda is represented i Characteristic value lambda representing the i-th evaluation object k Characteristic values k=1, 2, representing the kth evaluation principal component factor m The characteristic value of the correlation coefficient matrix is represented, m represents the number of evaluation objects, r represents the number of determined principal component factors, and r is usually more than or equal to 5 in order to ensure the scientificity of results and the integrity of an index system.
After the accumulated contribution of each principal component factor is obtained according to the method, extracting the principal component factor, performing factor rotation, and reconstructing factor load according to the internal structural relation; thus, the variable load on each principal factor approaches one of the extremum 0 and 1. The rotation axis increases the difference between the load of each variable over the same factor and reduces the difficulty of interpreting and determining the principal component factors.
Specifically, before the dimension reduction calculation is performed on the indexes of each subsystem by the principal component analysis method, the method further comprises the step of performing standardization processing on the indexes by using a range conversion method, wherein the range conversion formula is as follows:
if the evaluation index is a forward index, that is, the larger the numerical value is, the better the score value of the evaluation index is obtained through calculation according to the following formula:
and when V ij >10 is taken as V ij A value;
if the evaluation index is a negative index, namely, the smaller the numerical value is, the better the value is, the score value of the evaluation index is obtained according to the following formula:
and when V ij <When 0, 0 is taken as V ij A value;
in the above, V ij A score representing the i-th evaluation object under the j-th index in the evaluation index matrix V; min i (x ij ) Represents the lower limit value, m, of the category standard of the ith evaluation object under the jth index in the evaluation index matrix Vax i (x ij ) Represents the upper limit value, x of the class standard of the ith evaluation object under the jth index in the evaluation index matrix V ij The original number of the ith evaluation object under the jth index in the evaluation index matrix V is represented.
And then, extracting principal component factors from the standardized data by using a principal component analysis method, wherein the Bartlett sphericity test and KMO are used for testing whether the number of clusters is reasonable, and alpha=0.05 is used as a given significance level value. When the significance coefficient sig is more than 0.05, no significance difference exists among indexes, and the main component factors are reasonably extracted; when sig is less than 0.05, the indexes have obvious difference, and the extraction of main component factors is unreasonable.
In this embodiment, the entropy weight-principal component analysis method is as follows:
calculating the proportion of the ith object to be evaluated under the jth index in the matrix V:
wherein ,Ej Entropy of the j index is represented, and E is 0-E j ≤1；
Calculating an index difference coefficient h j ：
h j ＝1-E j
In particular, if v ij Smaller difference of E j Larger, h j Smaller; thus, the j-th index has less effect in the evaluation and vice versa. h is a j After normalization, the weight W used as the j index in the i-th evaluation object ij ：
The entropy weight method can process different data matrixes so as to achieve the purposes of retaining important information and reducing redundancy by utilizing entropy differences. As an important application, the entropy weight method is combined with the principal component analysis method, and entropy information and covariance information are loaded so as to achieve a better identification effect. Meanwhile, an entropy weight method is introduced into principal component analysis, so that objectivity and fairness of an evaluation result can be improved. And combining the characteristics of the two methods, establishing a coupling entropy weight principal component analysis model according to the following principles and standards.
Specifically, assume y k (k=1, 2,) r is x k E X (k=1, 2,., r) based on a weight W ij Formula, y k Stretching into column vector of D×1 (D=m×n), and the covariance matrix is
wherein 
Feature vector->
Corresponding to the eigenvalues of the first k principal component factors, a transformation matrix of Ixi can be formed>
The characteristic value score of the water resource bearing capacity can be calculated by the following formula:
wherein Y= [ Y ] 1 ,y 2 ,...,y r ]，
Is a matrix of j x r.
wherein ,
is the kth in the ith evaluation objectScore of each principal component factor, n represents total number of evaluation index,/->
Represents a matrix of j x r,/j>
Represents a matrix of j x j, Y represents a matrix of r x r, r represents the number of principal component factors.
S3, evaluating the bearing capacity of the target water resource by using a coupling TOPSIS method based on an entropy weight-principal component analysis model, wherein the implementation method is as follows:
s301, determining positive and negative ideal solutions and distances and proximity of each evaluation object to the ideal solutions through a TOPSIS method according to an entropy weight-principal component analysis model and an evaluation index system;
s302, constructing a coupling entropy weight main component-TOPSIS model according to positive and negative ideal solutions and the distance and the closeness between each evaluation object and the ideal solution;
s303, evaluating the bearing capacity of the target water resource by utilizing an entropy weight main component-TOPSIS model.
Specifically, positive and negative ideal solutions, and distances and closeness between each evaluation object and the ideal solution are determined through a TOPSIS method based on an index system. The TOPSIS method is a method for sorting according to the proximity degree of a limited number of evaluation objects and an idealized target, wherein the relative good and bad evaluation is carried out in the existing objects, the sorting is carried out by detecting the distances between the evaluation objects and the optimal solution and between the evaluation objects and the worst solution, and the method is the best if the evaluation objects are closest to the optimal solution and are farthest from the worst solution; otherwise, not optimal. Wherein each index value of the optimal solution reaches the optimal value of each evaluation index. Each index value of the worst solution reaches the worst value of each evaluation index. In accordance with the following principles and standards.
Determining positive and negative ideal solutions:
determining each evaluation object and theoryDistance of desired solution, positive ideal solution distance is
Negative ideal solution distance is->
Construction of relative proximity value E i ：
Establishing a coupling entropy weight TOPSIS-PCA model
In particular, the method comprises the steps of,
score representing the kth principal component factor in the ith evaluation object, +.>
To ensure that its value is at [0,1 ]]And (3) inner part.
wherein ,
and (3) representing the entropy weight of the kth principal component factor in the ith evaluation object and the comprehensive score of the principal component factor score.
Coupling principal component analysis for optimization evaluation-entropy weight-TOPSIS model:
wherein ,
the i-th evaluation target final overall evaluation score is represented by b, which represents a coefficient 0.ltoreq.b.ltoreq.1, and usually 0.6.
After the comprehensive score values of the water resource bearing capacity of the evaluation areas are obtained according to the above method, the water resource bearing states in the evaluation areas can be determined according to the following level criteria of table 2, and table 2 is a classification criterion of the water resource bearing states.
TABLE 2
For example, if the comprehensive score of the water resource bearing capacity of the evaluation area a is 4.1 points and the comprehensive score of the water resource bearing capacity of the evaluation area B is 8 points, the water resource bearing state of the area a is a critical overload state, and the water resource bearing state of the area B is a safe bearing state.
In order to verify that the method provided by the invention can evaluate the water resource bearing condition of a specific research area, a certain secondary drainage basin is selected as a verification area, and the method is used as a specific application scene of the invention to identify the change condition of the water resource bearing capacity of the research area 2020.
According to the collected high-frequency index combined with the research area collected data, 22 indexes are selected to construct an initial index library; and the indexes are subdivided into 4 subsystems such as hydrological weather, socioeconomic, ecological environment, hydraulic engineering and the like, as shown in table 3, and table 3 is an evaluation index system table.
TABLE 3 Table 3
The correlation matrix eigenvalues of the 22 indices are determined by principal component analysis. The process analyzes and extracts the principal component factors and gives the characteristic value contribution rate and the accumulated contribution rate of the coefficient matrix; the first 7 factors with eigenvalues greater than 1 were chosen as the main factors, as shown in table 4:
TABLE 4 Table 4
The 22 index variables were orthogonally rotated to obtain a rotated transformation matrix, as shown in table 5:
TABLE 5
After the entropy value of 7 main component factors extracted from the index system is calculated, the entropy weight is finally obtained, as shown in table 6:
TABLE 6
Based on table 6, each index entropy weight-principal component score is calculated as shown in table 7:
TABLE 7
Based on the above steps, the matrix X is normalized to obtain a matrix V, and then the entropy E of each index is calculated j And weight W j And determining an optimal solution D of the standard weighting matrix D + And worst solution D - As shown in table 8:
TABLE 8
Determining Euclidean distance between 22 index values and the optimal solution or the inferior solution, and finally determining the proximity E between the index values and the optimal solution i The method comprises the steps of carrying out a first treatment on the surface of the And score the principal component of each evaluation object
Transforming to obtain a new score +.>
And calculating the comprehensive evaluation value of the coupled PCA and entropy weight TOPSIS method according to the formula>
The comprehensive score of the water resource bearing capacity of the study area in the year is 5.47, and the water resource bearing capacity of the study area in the year is in a critical state according to the classification standard of the table 2.
In the technical scheme, each evaluation index in the hydrological subsystem, the socioeconomic subsystem, the ecological environment subsystem and the hydraulic engineering subsystem is actual numerical data obtained by adopting various technical means or other methods in an evaluation area, so that the obtained evaluation result of the water resource bearing capacity reflects the actual condition of the evaluation area. In the present embodiment, however, according to actual numerical data of each evaluation index in the hydrological subsystem, the socioeconomic subsystem, the ecological environment subsystem, and the hydraulic engineering subsystem as a basis, the change trend of each index in a certain time in the future is combined, so that the prediction data of each index in a certain time in the future can be obtained.
The prediction data of each evaluation index in the hydrological subsystem, the socioeconomic subsystem, the ecological environment subsystem and the hydraulic engineering subsystem in a certain time in the future are substituted, so that the evaluation result of the water ecological bearing capacity of the evaluation area in a certain time in the future can be obtained; the method can predict the water resource bearing capacity of the evaluation area in a certain time in the future, can analyze which indexes have important influence on the water resource bearing capacity in a certain time in the future by combining the prediction result with the change of the existing data compared with the prediction data of various indexes, belongs to key variables, and can guide practice to dynamically regulate and control, so that the optimal result of the water resource bearing capacity is obtained in a certain time in the future.
In summary, the invention obtains the corresponding water resource bearing capacity (the function value output by the functional relation) by inputting the index value of the target index into the index bearing capacity functional relation. The water resource bearing capacity is calculated by establishing the coupling model, so that the accuracy of the water resource bearing capacity obtained by the method can be improved. And further, the water resource bearing capacity obtained by the invention can better provide technical support for developing water resources.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. The water resource bearing capacity evaluation method is characterized by comprising the following steps of:
s1, constructing an evaluation index system, and dividing the evaluation index system into different subsystem indexes according to index characteristics;
s2, performing entropy weight principal component dimension reduction processing on each subsystem index, and constructing an entropy weight-principal component analysis model;
and S3, evaluating the bearing capacity of the target water resource by using a coupling TOPSIS method based on the entropy weight-principal component analysis model.
2. The water resource load bearing capacity evaluation method according to claim 1, wherein the step S1 comprises the steps of:
s101, collecting water resource area indexes in a designated area;
s102, constructing an evaluation index system based on the water resource area index, and dividing the evaluation index system into different subsystem indexes according to index characteristics.
3. The water resource load bearing capacity evaluation method according to claim 1, wherein the step S2 comprises the steps of:
s201, carrying out standardization processing on each subsystem index by utilizing an entropy weight principal component analysis method to obtain a score value of an evaluation index, and carrying out dimension reduction calculation based on the score value, wherein the number of principal component factors is more than 5;
s202, based on the dimension reduction result, checking whether the index data passes the significance test, if so, entering a step S203, otherwise, returning to the step S201;
s203, determining the contribution degree of each principal component factor according to the judging result, loading entropy information and covariance information by a coupling entropy weight method, and constructing an entropy weight-principal component analysis model.
4. The water resource load bearing capacity evaluation method according to claim 3, wherein the normalization process in step S201 is as follows:
if each subsystem index is a forward index, the score value of the evaluation index is obtained according to the following formula:
if each subsystem index is a negative index, the score value of the evaluation index is obtained according to the following formula:
wherein ,Vij The score of the ith evaluation object under the jth index in the evaluation index matrix V is represented as V ij >10 is taken as V ij When V is the value of ij <When 0, 0 is taken as V ij >10 value, min i (x ij ) Represents the lower limit value, max, of the category standard of the ith evaluation object under the jth index in the evaluation index matrix V i (x ij ) Represents the upper limit value, x of the class standard of the ith evaluation object under the jth index in the evaluation index matrix V ij The original number of the ith evaluation object under the jth index in the evaluation index matrix V is represented.
5. The water resource load bearing capacity evaluation method according to claim 4, wherein the expression of the entropy weight-principal component analysis model in step S203 is as follows:
h j ＝1-E j
Y＝[y 1 ,y 2 ,...,y r ]
wherein ,Ej Entropy of the j index is represented, and E is 0-E j Less than or equal to 1, n represents the number of evaluation indexes, m represents the number of evaluation objects, h j Representing the number difference coefficient, W j Weight, W representing any index of the ith evaluation object ij The weight of the j index in the i-th evaluation object is represented,
represents a matrix of j x r,/j>
Represents a matrix of j x j, Y represents a matrix of r x r, r represents the number of principal component factors,/->
Representation->
The point vector of the j-th index of (b), y r Point vector representing the r-th principal component factor in Y,/->
The score of the kth principal component factor in the ith evaluation object, k=1, 2.
6. The water resource load bearing capacity evaluation method according to claim 5, wherein the step S3 comprises the steps of:
s301, determining positive and negative ideal solutions and distances and proximity of each evaluation object to the ideal solutions through a TOPSIS method according to an entropy weight-principal component analysis model and an evaluation index system;
s302, constructing a coupling entropy weight main component-TOPSIS model according to positive and negative ideal solutions and the distance and the closeness between each evaluation object and the ideal solution;
s303, evaluating the bearing capacity of the target water resource by utilizing an entropy weight main component-TOPSIS model.
7. The water resource load bearing capacity evaluation method according to claim 6, wherein the expressions of the positive and negative ideal solutions and the distances and the proximity of each evaluation object to the ideal solution are as follows:
wherein ,X+ Representing the positive ideal solution, X - Representing a negative ideal solution to the problem of,
positive ideal meaning of the nth index, < +.>
Negative ideal solution representing the nth index, < ->
Representing the positive ideal solution distance, +.>
The negative ideal solution distance is represented, n represents the number of indexes, j=1, 2,.. i Representing ideal solution proximity.
8. The water resource load bearing capacity evaluation method according to claim 7, wherein the expression of the entropy weight principal component-TOPSIS model is as follows:
wherein ,
represents the final overall evaluation score of the i-th evaluation object, b represents the coefficient, < ->
Score representing kth principal component factor in ith evaluation object,/for>
The entropy weight of the kth principal component factor in the ith evaluation object is represented as a composite score of the principal component factor scores, and r represents the number of principal component factors. />
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117649315A (en) * | 2023-12-22 | 2024-03-05 | 中国水利水电科学研究院 | Space balance degree evaluation method and device based on water resource allocation |
| CN118052373A (en) * | 2024-04-15 | 2024-05-17 | 广东海洋大学 | Ocean resource environment bearing capacity assessment method |
| CN118171493A (en) * | 2024-05-13 | 2024-06-11 | 河海大学 | Water resource bearing capacity prediction and scenario simulation method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117649315A (en) * | 2023-12-22 | 2024-03-05 | 中国水利水电科学研究院 | Space balance degree evaluation method and device based on water resource allocation |
| CN118052373A (en) * | 2024-04-15 | 2024-05-17 | 广东海洋大学 | Ocean resource environment bearing capacity assessment method |
| CN118052373B (en) * | 2024-04-15 | 2024-07-02 | 广东海洋大学 | Ocean resource environment bearing capacity assessment method |
| CN118171493A (en) * | 2024-05-13 | 2024-06-11 | 河海大学 | Water resource bearing capacity prediction and scenario simulation method |
| CN118171493B (en) * | 2024-05-13 | 2024-08-16 | 河海大学 | Water resource bearing capacity prediction and scenario simulation method |
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