CN116128303A - Network risk assessment method for watershed water and soil resource system - Google Patents
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Abstract
The invention discloses a network risk assessment method of a watershed water and soil resource system, which belongs to the technical field of watershed water and soil resource risk assessment and comprises the following steps: obtaining a network risk source of the water and soil resource system according to the risk factors of the water and soil resource system; constructing a water and soil resource system network risk evaluation index system based on a water and soil resource system network risk source; performing risk area division on the water and soil resource system network based on a water and soil resource system network risk evaluation index system and a cluster analysis-ISODATA algorithm to obtain a divided water and soil resource system network risk area; calculating the relative proximity corresponding to the partitioned water and soil resource system network risk areas based on an entropy weight-TOPSIS evaluation method to obtain a watershed water and soil resource system network risk evaluation result; the invention solves the problem of lack of a risk evaluation system for comprehensive consideration of water and soil resources.
Description
Technical Field
The invention belongs to the technical field of drainage basin water and soil resource risk assessment, and particularly relates to a drainage basin water and soil resource system network risk assessment method.
Background
Under the environment taking global climate warming and human activities as main characteristics, the problems of uneven spatial and temporal distribution of water resources, frequent water and drought disasters, water resource shortage, water ecological damage and water environment pollution are prominent; a series of human activities such as forest cutting, town expansion, wetland reclamation, etc. cause serious damage to land resources, resulting in atrophy of high-level vegetation landscapes and reduced ecological integrity. Along with the continuous transitional utilization and development of water and soil resources, serious water and soil resource shortage, water and soil ecology and other problems are generated, and the current problem of how to solve the contradiction between reasonable development and utilization of water and soil resources and sustainable development of socioeconomic performance becomes a key.
At present, the water resource evaluation focuses on aspects of water quantity, water quality, water ecology and the like in a water resource system, and the land resource evaluation focuses on aspects of scale, layout, ecological health and the like of land resources. The method is characterized in that from the basis of the basic attribute of the water and soil resources, the pressure of the comprehensive influence factors such as a social and economic system, a water and soil resource system, an ecological environment system and the like on the water and soil resource system network is considered, the influence of multiple factors on the water and soil resource system is recognized by the system, a network risk evaluation index system of the water and soil resource system of the system is constructed, and the network risk of the water and soil resource system is comprehensively evaluated.
Disclosure of Invention
Aiming at the defects in the prior art, the risk assessment method for the water and soil resource system network in the river basin provided by the invention comprehensively considers the climate change, the human activities and the mutual feedback mechanism of water and soil resources, carries out risk assessment on the water and soil resource system network in the river basin from the pixel scale, and solves the problem of a risk assessment system lacking comprehensive consideration of water and soil resources.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention provides a network risk assessment method of a watershed soil and water resource system, which comprises the following steps:
s1, obtaining a network risk source of the water and soil resource system according to risk factors of the water and soil resource system;
s2, constructing a network risk evaluation index system of the water and soil resource system based on the network risk source of the water and soil resource system;
s3, performing risk area division on the water and soil resource system network based on a water and soil resource system network risk evaluation index system and a cluster analysis-ISODATA algorithm to obtain a divided water and soil resource system network risk area;
and S4, calculating the relative proximity corresponding to the divided water and soil resource system network risk areas based on the entropy weight-TOPSIS evaluation method to obtain a watershed water and soil resource system network risk evaluation result.
The beneficial effects of the invention are as follows: the method for evaluating the network risk of the watershed water and soil resource system provided by the invention is based on the construction concept of a 'rich nature-function coordination watershed', takes a 'mountain water Lin Tianhu grass' system as a basis, comprehensively considers the mutual feedback mechanism of climate change, human activities and water and soil resources from the basic attribute of the water and soil resources, selects evaluation indexes such as 'quality', 'effect', 'life', and the like, creates the network risk evaluation method of the watershed water and soil resource system based on the mutual feedback mechanism, and realizes evaluation of the network risk of the watershed water and soil resource system from the pixel scale, thereby being an important support for national-soil space optimization, water resource safety guarantee and ecological construction.
Further, the step S1 includes the steps of:
s11, obtaining a local water supply risk factor according to the surface water resource quantity, the underground water resource quantity and the reclaimed water;
s12, obtaining an external water diversion risk factor according to the amount of the water diversion resources and the amount of the water diversion resources;
s13, respectively obtaining a production water risk factor, a domestic water risk factor and an ecological water risk factor according to agricultural water, industrial water, third industrial water, town domestic water, rural domestic water, natural ecological water and artificial ecological water;
S14, obtaining land scale risk factors according to land area changes of cultivated land, forest land, grassland, water area, land occupation and unused land areas;
s15, obtaining land formation risk factors according to agricultural lands, industrial and domestic lands and ecological lands;
s16, obtaining a land layout risk factor according to the integrity, the fragmentation degree and the shannon diversity index change of land resources;
s17, obtaining land function risk factors according to NPP, water conservation capacity, annual runoff variation coefficient CV regulation capacity and satisfaction degree of slope ecological water demand and river ecological water demand;
s18, obtaining a water and soil resource system network risk source according to the local water supply risk factor, the external water transfer risk factor, the production water risk factor, the domestic water risk factor, the ecological water risk factor, the land scale risk factor, the land construction risk factor, the land layout risk factor and the land function risk factor.
The beneficial effects of adopting the further scheme are as follows: the system identifies the influence of multiple factors on the soil and water resource system by considering the pressure of the comprehensive influence factors such as climate change, a social and economic system, a soil and water resource system, an ecological environment system and the like on the soil and water resource system network from the basic attribute of the soil and water resource, and provides a basis for the network risk assessment index system of the soil and water resource system.
Further, the step S2 includes the steps of:
s21, establishing a person average water consumption evaluation index according to the person average domestic water consumption spread on the construction site:
wherein ,Wpeople Represents the water consumption of people and W life The water consumption is represented, the peoples represent the population number, and the Area represents the Area of the construction site;
s22, establishing a ten-thousand-yuan GDP water consumption evaluation index according to the ten-thousand-yuan GDP water consumption of the living sites and the cultivated lands:
wherein ,WGDP Represents the water consumption of ten thousand yuan GDP, W production The total amount of industrial water and the total amount of agricultural water are represented, GDP represents the total domestic production value, area 1 Representing the area of a living site and cultivated land;
s23, constructing an ecological construction water consumption evaluation index according to the water consumption spread on the ecological construction land:
wherein ,We Represents the water consumption for ecological construction, W ecology Representing the total amount of ecological construction water, area 2 Representing an ecological construction land area;
s24, respectively establishing a pollution amount evaluation index in unit area, a population density evaluation index in unit area and a ten-thousand-yuan GDP evaluation index in unit area according to the point source pollution total amount and the surface source pollution total amount which are spread and distributed on the construction sites and the cultivated lands, the population density of the construction sites and the GDP generated on the construction sites and the cultivated lands:
wherein ,Ws Represents the pollution amount per unit area, W sewage Indicating the total amount of point source pollution and non-point source pollution, L d Represents population density per unit area, L GDP Represents a ten thousand-membered GDP per unit area;
s25, taking the shannon diversity index in the landscape pattern index as a main index for quantitative evaluation of the ecological fragmentation degree, and constructing an ecological integrity evaluation index:
wherein SHDI represents shannon diversity index, n represents total plaque type number in landscape, P k Representing the proportion of the kth plaque to the total landscape area;
s26, constructing a unit evaporation NPP evaluation index according to NPP generated by unit area evaporation:
wherein ,LNPP NPP generated in unit area, NPP represents vegetation net primary productivity, evap represents regional evaporation amount;
s27, constructing a water production coefficient evaluation index according to the water production coefficient:
wherein, C represents the regional water production coefficient, water represents the total regional water resource, and Pre represents the total regional precipitation;
s28, constructing a CV variation coefficient evaluation index according to the CV variation coefficient of the water production coefficient:
wherein CV represents a CV value representing a water production coefficient, sd represents a standard deviation of the water production coefficient, mean represents an average value of the water production coefficient;
s29, constructing a network risk evaluation index system of the water and soil resource system based on the average water consumption evaluation index, the ten thousand-element GDP water consumption evaluation index, the ecological construction water consumption evaluation index, the unit area pollution amount evaluation index, the unit area population density evaluation index, the unit area ten thousand-element GDP evaluation index, the ecological integrity evaluation index, the unit evaporation NPP evaluation index, the water production coefficient evaluation index and the CV variation coefficient evaluation index.
The beneficial effects of adopting the further scheme are as follows: based on the network risk source identification of the water and soil resource system, the average water consumption, the ten thousand yuan GDP water consumption, the ecological construction water consumption, the unit area pollution production, the population density on the unit area, the unit area ten thousand yuan GDP, the ecological integrity, the NPP generated by unit evapotranspiration, the water production coefficient and the water production coefficient CV value are selected as the network risk assessment index of the water and soil resource system, the network risk assessment index system of the water and soil resource system is constructed, and the network risk of the water and soil resource system is assessed from multiple angles and multiple factors.
Further, the step S3 includes the following steps:
s31, acquiring n' water and soil resource risk evaluation index samples based on a water and soil resource system network risk evaluation index system, and selecting w initial clustering centers based on a clustering analysis-ISODATA algorithm:
X＝{x i ,i＝1,2,...,n′}
A＝{a j ,j＝1,2,...,w}
wherein X represents a sample set of water-soil resource risk evaluation indexes, and X i The i-th water and soil resource risk evaluation index sample is represented, n' represents the total number of the water and soil resource risk evaluation index samples, A represents the initial clustering center set, and a j Representing the j-th initial cluster center, w representing the total number of the initial cluster centers;
s32, defining the minimum value of the water and soil resource risk evaluation index sample number in each initial clustering center as Q, the minimum distance between any two initial clustering centers as d, the maximum variance as S, the preset clustering center number as R and the maximum iteration number as L;
S33, respectively calculating the distance from each water-soil resource risk evaluation index sample to each initial clustering center, and adding each waterMerging soil resource risk evaluation index samples to a class D corresponding to an initial clustering center with the smallest distance j In (a) and (b);
s34, for category D j If the water and soil resource risk evaluation index sample is smaller than Q, giving up category D j Let the initial cluster center total w=w-1, and let class D j The water and soil resource risk evaluation index samples in the model are re-merged into the categories corresponding to the initial clustering centers with the minimum distance, so that the partitioned clustering centers are obtained;
s35, respectively calculating the distance between any two divided clustering centers when w is smaller than or equal to R/2, so as to obtain a clustering center distance matrix P;
s36, merging two cluster centers corresponding to any cluster center distance element in the cluster center distance matrix P when the cluster center distance element is smaller than d, so as to obtain a merged cluster center;
s37, respectively calculating variances of the water and soil resource risk evaluation index samples in all the categories corresponding to the clustering centers under each dimension when w is greater than or equal to 2R, so as to obtain the maximum variances in the categories;
s38, splitting the clustering center to obtain a first sub-clustering center and a second sub-clustering center after splitting when the maximum variance is greater than S and the number of samples contained in the class is greater than 2 times of the preset minimum number of samples, wherein w=w+1:
D m + ＝D m +θ max
D m - ＝D m -θ max
wherein ,Dm + Represents a first sub-cluster center, D m Representing the cluster center of the split, θ max Represents the maximum variance, D m - Representing a second sub-cluster center;
s39, repeating the steps S33 to S38 until the repetition times reach L, and completing the risk area division of the water and soil resource system network to obtain a divided water and soil resource system network risk area.
The beneficial effects of adopting the further scheme are as follows: the cluster analysis-ISODATA algorithm has the advantages that the cluster analysis-ISODATA algorithm is insensitive to noise data in a large data set, the number of categories and the category center can be dynamically adjusted, and 5 types of cluster analysis results corresponding to 5 risk levels of high risk, medium risk, low risk and low risk are obtained through the cluster analysis-ISODATA algorithm on a constructed water and soil resource system network risk assessment index system.
Further, the calculation expression of the cluster center divided in the step S34 is as follows:
wherein ,aj′ And (3) representing the j' th divided clustering center, and x represents a water-soil resource risk evaluation index sample.
The beneficial effects of adopting the further scheme are as follows: the specific calculation method of the partitioned cluster centers is provided, and a foundation is provided for merging and splitting of the cluster centers.
Further, the calculation expression of the cluster center after merging in the step S36 is as follows:
wherein E represents the cluster center after combination, n i ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the first cluster center for merging, n j ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the second cluster center for merging, D i″ Representing the class corresponding to the first cluster center for merging, D j″ Representing the category corresponding to the second cluster center for merging.
The beneficial effects of adopting the further scheme are as follows: the concrete calculation method of the combined clustering center is provided, and a foundation is provided for obtaining the network risk area of the partitioned water and soil resource system.
Further, the step S4 includes the steps of:
s41, constructing an initial data evaluation matrix based on the average value of the water and soil resource risk evaluation index samples in the classified water and soil resource system network risk area:
wherein Y represents an initial data evaluation matrix, Y pq The q-th watershed water and soil resource risk assessment index of the p-th class is represented;
s42, normalizing water and soil resource risk assessment indexes of each river basin in the initial data assessment matrix:
wherein ,y+ Representing normalized forward river basin water and soil resource risk assessment index, y - Representing normalized negative drainage basin water and soil resource risk assessment index, y p′ Representing the water and soil resource risk assessment index of the p' th class river basin, y p′min Representing the minimum value, y of the water and soil resource risk assessment index of the p' th class river basin p′max Representing the maximum value of the water and soil resource risk assessment index of the p' type river basin;
s43, respectively carrying out weight calculation on the normalized water and soil resource risk assessment indexes of each river basin based on an entropy weight-TOPSIS evaluation method to obtain the weights of the water and soil resource risk assessment indexes of each river basin:
wherein ,eq″ The weight of the q' th basin water and soil resource risk assessment index is represented, k represents a weight constant coefficient, b p″q″ Representing the duty ratio of the p 'th category in the q' th basin water and soil resource risk assessment index;
s44, based on the weights of the water and soil resource risk assessment indexes of the drainage basins, respectively calculating the entropy weights of the water and soil resource risk assessment indexes of the drainage basins:
wherein ,wq″ Entropy weight of the water and soil resource risk assessment index of the q' th river basin is represented;
s45, obtaining a weight combination vector based on the entropy weight of each river basin water and soil resource risk assessment index:
W＝(w 1 ,w 2 ,...,w q )
wherein W represents a weight combination vector, W q A weight vector representing a water and soil resource risk assessment index of the q-th river basin;
S46, standardized processing of water and soil resource risk assessment indexes of each river basin:
wherein ,Ap″q″ The q 'th basin water and soil resource risk assessment index of the p' th class after standardized treatment is represented;
s47, carrying out standardization processing on the water and soil resource risk assessment indexes and the weight combination vectors of each river basin to obtain a weighted standardization matrix:
z p″q″ ＝w q″ A p″q″ ,z p″q″ ∈Z
wherein Z represents a weighted normalization matrix, w q″ Weight vector z representing q' th basin water and soil resource risk assessment index p″q″ Representing the elements of the p "th row and q" th column in the weighted normalization matrix, A pq The q-th river basin water and soil resource risk assessment index of the p-th class after the standardized treatment is represented;
s48, calculating to obtain the Euclidean distance of the optimal solution and the worst solution of each water and soil resource evaluation object based on the weighted standardized matrix:
wherein ,
euclidean distance representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Euclidean distance representing the worst solution of the q' th water and soil resource evaluation object, < ->
Representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Represents the worst solution of the q' th water and soil resource evaluation object S p″q″ Solution representing p-th "q-th" water and soil resource evaluation object, ++>
Representing the optimal solution of the q-th water and soil resource evaluation object >
Representing the worst solution of the q-th water and soil resource evaluation object;
s49, constructing the relative proximity corresponding to the partitioned water and soil resource system network risk areas based on the Euclidean distance of the optimal solution and the worst solution of each water and soil resource evaluation object, and sorting the relative proximity from big to small to obtain a basin water and soil resource system network risk evaluation result with risk levels corresponding to the sorting from small to big.
The beneficial effects of adopting the further scheme are as follows: the method comprises the steps of constructing an original DATA evaluation matrix, calculating 5 kinds of clustering analysis results and 10 network risk evaluation indexes of a water and soil resource system by an ISO-DATA algorithm, combining the advantages of the entropy weight TOPSIS evaluation method and the TOPSIS method, effectively eliminating partial errors caused by human factors, calculating information entropy values, information utility values, weight coefficients, standard deviations, positive ideal solutions and negative ideal solutions of the evaluation indexes by the entropy weight TOPSIS method, and finally carrying out risk rank ordering on the 5 kinds of clustering analysis results according to the proximity degree, and dividing the 5 kinds of clustering analysis results into high risk, medium and low risk in sequence.
Further, the calculation expression of the relative proximity in step S49 is as follows:
wherein ,
representing relative proximity.
The beneficial effects of adopting the further scheme are as follows: the specific calculation method of the relative proximity is provided, the greater the obtained relative proximity is, the lower the risk level of the corresponding evaluation object is, and a basis is provided for obtaining the network risk evaluation result of the watershed water-soil resource system with the risk level being ranked from small to large through the relative proximity.
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Fig. 1 is a flowchart of steps of a network risk assessment method for a drainage basin water-soil resource system in an embodiment of the present invention.
Fig. 2 is a diagram of a network risk assessment result of a water-soil resource system in a flow domain in an embodiment of the 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.
As shown in fig. 1, in one embodiment of the present invention, data analysis and risk assessment are performed based on land utilization, water consumption, socioeconomic data, meteorological data and NPP remote sensing image data in Huang-Huai-Hai river basin 2011-2020, and the present invention provides a river basin water and soil resource system network risk assessment method, which includes the following steps:
S1, obtaining a network risk source of the water and soil resource system according to risk factors of the water and soil resource system;
the step S1 includes the steps of:
s11, obtaining a local water supply risk factor according to the surface water resource quantity, the underground water resource quantity and the reclaimed water;
s12, obtaining an external water diversion risk factor according to the amount of the water diversion resources and the amount of the water diversion resources;
s13, respectively obtaining a production water risk factor, a domestic water risk factor and an ecological water risk factor according to agricultural water, industrial water, third industrial water, town domestic water, rural domestic water, natural ecological water and artificial ecological water;
s14, obtaining land scale risk factors according to land area changes of cultivated land, forest land, grassland, water area, land occupation and unused land areas;
s15, obtaining land formation risk factors according to agricultural lands, industrial and domestic lands and ecological lands;
s16, obtaining a land layout risk factor according to the integrity, the fragmentation degree and the shannon diversity index change of land resources;
s17, obtaining land function risk factors according to NPP, water conservation capacity, annual runoff variation coefficient CV regulation capacity and satisfaction degree of slope ecological water demand and river ecological water demand;
S18, obtaining a water and soil resource system network risk source according to a local water supply risk factor, an external water transfer risk factor, a production water risk factor, a domestic water risk factor, an ecological water risk factor, a land scale risk factor, a land construction risk factor, a land layout risk factor and a land function risk factor;
the water resource system, the land resource system and the network risk source of the water and soil resource system are identified, so that theoretical support is provided for construction of a network risk assessment index system of the land resource system;
s2, constructing a network risk evaluation index system of the water and soil resource system based on the network risk source of the water and soil resource system;
in the embodiment, ten evaluation indexes of water and soil resource risks can be reflected by comprehensively selecting water consumption per person, water consumption of ten-thousand-yuan GDP, ecological construction water consumption, sewage yield per unit area, number per unit area and water yield coefficient CV value, wherein the water consumption per ten-thousand-yuan GDP, the ecological integrity, the NPP generated by unit evapotranspiration and the water and soil resource system network risk source identification are provided;
the step S2 includes the steps of:
S21, establishing a person average water consumption evaluation index according to the person average domestic water consumption spread on the construction site:
wherein ,Wpeople Represents the water consumption of people and W life The water consumption is represented, the peoples represent the population number, and the Area represents the Area of the construction site;
s22, establishing a ten-thousand-yuan GDP water consumption evaluation index according to the ten-thousand-yuan GDP water consumption of the living sites and the cultivated lands:
wherein ,WGDP Represents the water consumption of ten thousand yuan GDP, W production The total amount of industrial water and the total amount of agricultural water are represented, GDP represents the total domestic production value, area 1 Representing the area of a living site and cultivated land;
s23, constructing an ecological construction water consumption evaluation index according to the water consumption spread on the ecological construction land:
wherein ,We Represents the water consumption for ecological construction, W ecology Representing the total amount of ecological construction water, area 2 Representing an ecological construction land area;
s24, respectively establishing a pollution amount evaluation index in unit area, a population density evaluation index in unit area and a ten-thousand-yuan GDP evaluation index in unit area according to the point source pollution total amount and the surface source pollution total amount which are spread and distributed on the construction sites and the cultivated lands, the population density of the construction sites and the GDP generated on the construction sites and the cultivated lands:
wherein ,Ws Represents the pollution amount per unit area, W sewage Indicating the total amount of point source pollution and non-point source pollution, L d Represents population density per unit area, L GDP Represents a ten thousand-membered GDP per unit area;
s25, taking the shannon diversity index in the landscape pattern index as a main index for quantitative evaluation of the ecological fragmentation degree, and constructing an ecological integrity evaluation index:
wherein SHDI represents shannon diversity index, n represents total plaque type number in landscape, P k Representing the proportion of the kth plaque to the total landscape area;
s26, constructing a unit evaporation NPP evaluation index according to NPP generated by unit area evaporation:
wherein ,LNPP NPP generated in unit area, NPP represents vegetation net primary productivity, evap represents regional evaporation amount;
s27, constructing a water production coefficient evaluation index according to the water production coefficient:
wherein, C represents the regional water production coefficient, water represents the total regional water resource, and Pre represents the total regional precipitation;
s28, constructing a CV variation coefficient evaluation index according to the CV variation coefficient of the water production coefficient:
wherein CV represents a CV value representing a water production coefficient, sd represents a standard deviation of the water production coefficient, mean represents an average value of the water production coefficient;
s29, constructing a water and soil resource system network risk evaluation index system based on a water consumption average evaluation index, a water consumption ten thousand yuan GDP evaluation index, an ecological construction water consumption evaluation index, a pollution amount per unit area evaluation index, a population density per unit area evaluation index, a ten thousand yuan GDP evaluation index, an ecological integrity evaluation index, a steam distribution NPP evaluation index, a water production coefficient evaluation index and a CV variation coefficient evaluation index;
In the embodiment, 128 ground administrative areas of Huang-Huai river basin are taken as boundaries, and the population, GDP, domestic water consumption, ecological water consumption, industrial water consumption, agricultural water consumption and pollution discharge of all municipal levels in 2011-2020 are respectively counted based on an Arcgis platform; statistics of the area of the cultivated land, the area of the forest land, the area of the grassland, the water area, the occupied area and the area of the unused land in 2020, wherein the point source pollution is obtained by social statistics of the annual survey; as the main pollution sources of the non-point pollution come from livestock and poultry cultivation, towns and rural life, the non-point emission of the urban life, the rural life pollution sources, the livestock and poultry cultivation and the like in the current area is estimated by urban population, rural population and livestock quantity; in order to facilitate analysis and summarization of the loss of the livestock and poultry pollutants, the emission coefficient of the livestock and poultry pollutants is estimated for the non-data area; rural domestic pollution is estimated with reference to the following data: COD:50 g/person day; ammonia nitrogen: 3.2 g/man/day, wherein the ecological construction land is calculated based on land utilization in 2000 and 2020, and the forest land, the grassland and the water area increased in 2020 are regarded as the ecological construction land between 2000 and 2020 on the basis of land utilization in 2000; based on the index calculation formula, the average water consumption of people, the water consumption of ten thousand-yuan GDP, the water consumption of ecological construction, the sewage yield of unit area, the population density on unit area and the ten thousand-yuan GDP of unit area in 2011-2020 are calculated and obtained, and the space distribution data of 1km multiplied by 1km are spread through Arcgis software; the ecological integrity is based on 2020 land utilization, and SHDI spatial distribution indexes are obtained through calculation by using Fragstats software; the evaporation and NPP are spatial distribution data, and the spatial distribution data of the NPP generated by the unit evaporation of the Huang-Huai river basin 2011-2020 is obtained through calculation; the total water resource and precipitation amount are obtained by the Chinese water resource gazette to obtain 128 municipal administration area basic data, 2011-2020 municipal water production coefficient and water production coefficient CV value are obtained by calculation, and are spread to be 1km multiplied by 1km space distribution data by an Arcgis software platform;
S3, performing risk area division on the water and soil resource system network based on a water and soil resource system network risk evaluation index system and a cluster analysis-ISODATA algorithm to obtain a divided water and soil resource system network risk area;
carrying out cluster analysis on a network risk evaluation index system of the water and soil resource system with ten evaluation indexes by using a cluster analysis-ISODATA algorithm, and dividing the network risk of the water and soil resource system into 5 levels of high risk, medium and low risk, so that a 5-class cluster analysis result is finally obtained in the cluster analysis-ISODATA algorithm, namely, the divided network risk area of the water and soil resource system corresponds to the 5-class risk area;
the step S3 includes the steps of:
s31, acquiring n' water and soil resource risk evaluation index samples based on a water and soil resource system network risk evaluation index system, and selecting w initial clustering centers based on a clustering analysis-ISODATA algorithm:
X＝{x i ,i＝1,2,...,n′}
A＝{a j ,j＝1,2,...,w}
wherein X represents a sample set of water-soil resource risk evaluation indexes, and X i The i-th water and soil resource risk evaluation index sample is represented, n' represents the total number of the water and soil resource risk evaluation index samples, A represents the initial clustering center set, and a j Representing the j-th initial cluster center, w representing the total number of the initial cluster centers; in this embodiment, n' is 10 and w is 5;
s32, defining the minimum value of the water and soil resource risk evaluation index sample number in each initial clustering center as Q, the minimum distance between any two initial clustering centers as d, the maximum variance as S, the preset clustering center number as R and the maximum iteration number as L;
s33, respectively calculating the distance from each water and soil resource risk evaluation index sample to each initial clustering center, and merging each water and soil resource risk evaluation index sample to a class D corresponding to the initial clustering center with the minimum distance j In (a) and (b);
s34, for category D j If the water and soil resource risk evaluation index sample is smaller than Q, giving up category D j Let the initial cluster center total w=w-1, and let class D j The water and soil resource risk evaluation index samples in the model are re-merged into the categories corresponding to the initial clustering centers with the minimum distance, so that the partitioned clustering centers are obtained;
the calculation expression of the cluster center after the division in the step S34 is as follows:
wherein ,aj′ The j' th divided clustering center is represented, and x represents a water-soil resource risk evaluation index sample;
s35, respectively calculating the distance between any two divided clustering centers when w is smaller than or equal to R/2, so as to obtain a clustering center distance matrix P;
S36, merging two cluster centers corresponding to any cluster center distance element in the cluster center distance matrix P when the cluster center distance element is smaller than d, so as to obtain a merged cluster center;
the calculation expression of the cluster center after merging in the step S36 is as follows:
wherein E represents the cluster center after combination, n i ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the first cluster center for merging, n j ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the second cluster center for merging, D i″ Representing the class corresponding to the first cluster center for merging, D j″ Representing a category corresponding to a second cluster center for merging;
s37, respectively calculating variances of the water and soil resource risk evaluation index samples in all the categories corresponding to the clustering centers under each dimension when w is greater than or equal to 2R, so as to obtain the maximum variances in the categories;
s38, splitting the clustering center to obtain a first sub-clustering center and a second sub-clustering center after splitting when the maximum variance is greater than S and the number of samples contained in the class is greater than 2 times of the preset minimum number of samples, wherein w=w+1:
D m + ＝D m +θ max
D m - ＝D m -θ max
wherein ,Dm + Represents a first sub-cluster center, D m Representing the cluster center of the split, θ max Represents the maximum variance, D m - Representing a second sub-cluster center;
s39, repeating the steps S33 to S38 until the repetition times reach L, and completing the risk area division of the water and soil resource system network to obtain a divided water and soil resource system network risk area;
in this embodiment, based on a constructed network risk assessment system of a soil and water resource system, by using an Arcgis Pro software platform, taking the annual average value of an assessment index in 2011-2020 as an input sample of the network risk assessment of the soil and water resource system, setting 5 types of initial clustering centers, wherein the minimum allowed distance is initially 200, the maximum variance is initially 1000, and performing cluster analysis calculation by using an Arcgis software platform ISO cluster analysis tool to obtain 5 types of cluster analysis results, as shown in table 1:
TABLE 1
S4, calculating the relative proximity corresponding to the divided water and soil resource system network risk areas based on an entropy weight-TOPSIS evaluation method to obtain a watershed water and soil resource system network risk evaluation result;
in the embodiment, on the basis of 5 kinds of clustering analysis results, taking the average value of 10 evaluation indexes in each kind of analysis results as the input parameter of an entropy weight-TOPSIS evaluation method, and finally carrying out risk level sequencing on the 5 kinds of clustering analysis results according to the proximity degree, and sequentially dividing the 5 kinds of clustering analysis results into high risk, medium and low risk;
The step S4 includes the steps of:
s41, constructing an initial data evaluation matrix based on the average value of the water and soil resource risk evaluation index samples in the classified water and soil resource system network risk area:
wherein Y represents an initial data evaluation matrix, Y pq The q-th watershed water and soil resource risk assessment index of the p-th class is represented;
s42, normalizing water and soil resource risk assessment indexes of each river basin in the initial data assessment matrix:
wherein ,y+ Representing normalized forward river basin water and soil resource risk assessment index, y - Representing normalized negative drainage basin water and soil resource risk assessment index, y p′ Representing the water and soil resource risk assessment index of the p' th class river basin, y p′min Representing the minimum value, y of the water and soil resource risk assessment index of the p' th class river basin p′max Representing the maximum value of the water and soil resource risk assessment index of the p' type river basin;
s43, respectively carrying out weight calculation on the normalized water and soil resource risk assessment indexes of each river basin based on an entropy weight-TOPSIS evaluation method to obtain the weights of the water and soil resource risk assessment indexes of each river basin:
wherein ,eq″ The weight of the q' th basin water and soil resource risk assessment index is represented, k represents a weight constant coefficient, b p″q″ Representing the duty ratio of the p 'th category in the q' th basin water and soil resource risk assessment index;
S44, based on the weights of the water and soil resource risk assessment indexes of the drainage basins, respectively calculating the entropy weights of the water and soil resource risk assessment indexes of the drainage basins:
wherein ,wq″ Entropy weight of the water and soil resource risk assessment index of the q' th river basin is represented;
s45, obtaining a weight combination vector based on the entropy weight of each river basin water and soil resource risk assessment index:
W＝(w 1 ,w 2 ,...,w q )
wherein W represents a weight combination vector, W q A weight vector representing a water and soil resource risk assessment index of the q-th river basin;
s46, standardized processing of water and soil resource risk assessment indexes of each river basin:
wherein ,Ap″q″ The q 'th basin water and soil resource risk assessment index of the p' th class after standardized treatment is represented;
s47, carrying out standardization processing on the water and soil resource risk assessment indexes and the weight combination vectors of each river basin to obtain a weighted standardization matrix:
z p″q″ ＝w q″ A p″q″ ,z p″q″ ∈Z
wherein Z represents a weighted normalization matrix, w q″ Weight vector z representing q' th basin water and soil resource risk assessment index p″q″ Representing the elements of the p "th row and q" th column in the weighted normalization matrix, A pq The q-th river basin water and soil resource risk assessment index of the p-th class after the standardized treatment is represented;
s48, calculating to obtain the Euclidean distance of the optimal solution and the worst solution of each water and soil resource evaluation object based on the weighted standardized matrix:
wherein ,
euclidean distance representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Euclidean distance representing the worst solution of the q' th water and soil resource evaluation object, < ->
Representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Represents the worst solution of the q' th water and soil resource evaluation object S p″q″ Solution representing p-th "q-th" water and soil resource evaluation object, ++>
Representing the optimal solution of the q-th water and soil resource evaluation object>
Representing the worst solution of the q-th water and soil resource evaluation object;
s49, constructing the relative proximity corresponding to the partitioned water and soil resource system network risk areas based on the optimal solution and the worst solution of each water and soil resource evaluation object, and sorting the relative proximity from big to small to obtain a basin water and soil resource system network risk evaluation result with risk levels corresponding to the sorting from small to big;
the calculation expression of the relative proximity in step S49 is as follows:
wherein ,
representing relative proximity; the greater the relative proximity, the closer the evaluation object is to the optimal value, and the lower the risk level; />
As shown in fig. 2, the risk level ranking is performed on the 5-class clustering analysis results according to the proximity degree by the entropy weight-TOPSIS method, and the 5-class clustering analysis results are sequentially divided into high risk, medium and low risk, 2011-2020 entropy method results and 2011-2020 TOPSIS evaluation results are shown in table 2 and table 3 respectively:
TABLE 2
TABLE 3 Table 3
| Category(s) | Euclidean distance of optimal solution | Euclidean distance of the worst solution | Relative proximity of | Risk assessment ordering |
| First kind | 0.125 | 0.28 | 0.692 | 1 |
| Second kind | 0.143 | 0.228 | 0.614 | 2 |
| Third class | 0.217 | 0.21 | 0.491 | 4 |
| Class IV | 0.176 | 0.232 | 0.568 | 3 |
| Class V | 0.225 | 0.177 | 0.44 | 5 |
Claims (8)
1. A network risk assessment method for a river basin water and soil resource system is characterized by comprising the following steps:
s1, obtaining a network risk source of the water and soil resource system according to risk factors of the water and soil resource system;
s2, constructing a network risk evaluation index system of the water and soil resource system based on the network risk source of the water and soil resource system;
s3, performing risk area division on the water and soil resource system network based on a water and soil resource system network risk evaluation index system and a cluster analysis-ISODATA algorithm to obtain a divided water and soil resource system network risk area;
and S4, calculating the relative proximity corresponding to the divided water and soil resource system network risk areas based on the entropy weight-TOPSIS evaluation method to obtain a watershed water and soil resource system network risk evaluation result.
2. The method for evaluating the network risk of the basin-soil and water resource system according to claim 1, wherein the step S1 comprises the following steps:
s11, obtaining a local water supply risk factor according to the surface water resource quantity, the underground water resource quantity and the reclaimed water;
S12, obtaining an external water diversion risk factor according to the amount of the water diversion resources and the amount of the water diversion resources;
s13, respectively obtaining a production water risk factor, a domestic water risk factor and an ecological water risk factor according to agricultural water, industrial water, third industrial water, town domestic water, rural domestic water, natural ecological water and artificial ecological water;
s14, obtaining land scale risk factors according to land area changes of cultivated land, forest land, grassland, water area, land occupation and unused land areas;
s15, obtaining land formation risk factors according to agricultural lands, industrial and domestic lands and ecological lands;
s16, obtaining a land layout risk factor according to the integrity, the fragmentation degree and the shannon diversity index change of land resources;
s17, obtaining land function risk factors according to NPP, water conservation capacity, annual runoff variation coefficient CV regulation capacity and satisfaction degree of slope ecological water demand and river ecological water demand;
s18, obtaining a water and soil resource system network risk source according to the local water supply risk factor, the external water transfer risk factor, the production water risk factor, the domestic water risk factor, the ecological water risk factor, the land scale risk factor, the land construction risk factor, the land layout risk factor and the land function risk factor.
3. The method for evaluating the network risk of the basin-soil and water resource system according to claim 2, wherein the step S2 comprises the following steps:
s21, establishing a person average water consumption evaluation index according to the person average domestic water consumption spread on the construction site:
wherein ,Wpeople Represents the water consumption of people and W life The water consumption is represented, the peoples represent the population number, and the Area represents the Area of the construction site;
s22, establishing a ten-thousand-yuan GDP water consumption evaluation index according to the ten-thousand-yuan GDP water consumption of the living sites and the cultivated lands:
wherein ,WGDP Represents the water consumption of ten thousand yuan GDP, W production The total amount of industrial water and the total amount of agricultural water are represented, GDP represents the total domestic production value, area 1 Representing the area of a living site and cultivated land;
s23, constructing an ecological construction water consumption evaluation index according to the water consumption spread on the ecological construction land:
wherein ,We Represents the water consumption for ecological construction, W ecology Representing the total amount of ecological construction water, area 2 Representing an ecological construction land area;
s24, respectively establishing a pollution amount evaluation index in unit area, a population density evaluation index in unit area and a ten-thousand-yuan GDP evaluation index in unit area according to the point source pollution total amount and the surface source pollution total amount which are spread and distributed on the construction sites and the cultivated lands, the population density of the construction sites and the GDP generated on the construction sites and the cultivated lands:
wherein ,Ws Represents the pollution amount per unit area, W sewage Indicating the total amount of point source pollution and non-point source pollution, L d Represents population density per unit area, L GDP Represents a ten thousand-membered GDP per unit area;
s25, taking the shannon diversity index in the landscape pattern index as a main index for quantitative evaluation of the ecological fragmentation degree, and constructing an ecological integrity evaluation index:
wherein SHDI represents shannon diversity index, n represents total plaque type number in landscape, P k Representing the proportion of the kth plaque to the total landscape area;
s26, constructing a unit evaporation NPP evaluation index according to NPP generated by unit area evaporation:
wherein ,LNPP NPP generated in unit area, NPP represents vegetation net primary productivity, evap represents regional evaporation amount;
s27, constructing a water production coefficient evaluation index according to the water production coefficient:
wherein, C represents the regional water production coefficient, water represents the total regional water resource, and Pre represents the total regional precipitation;
s28, constructing a CV variation coefficient evaluation index according to the CV variation coefficient of the water production coefficient:
wherein CV represents a CV value representing a water production coefficient, sd represents a standard deviation of the water production coefficient, mean represents an average value of the water production coefficient;
s29, constructing a network risk evaluation index system of the water and soil resource system based on the average water consumption evaluation index, the ten thousand-element GDP water consumption evaluation index, the ecological construction water consumption evaluation index, the unit area pollution amount evaluation index, the unit area population density evaluation index, the unit area ten thousand-element GDP evaluation index, the ecological integrity evaluation index, the unit evaporation NPP evaluation index, the water production coefficient evaluation index and the CV variation coefficient evaluation index.
4. The method for evaluating the network risk of the basin-soil and water resource system according to claim 3, wherein the step S3 comprises the following steps:
s31, acquiring n' water and soil resource risk evaluation index samples based on a water and soil resource system network risk evaluation index system, and selecting w initial clustering centers based on a clustering analysis-ISODATA algorithm:
X＝{x i ,i＝1,2,...,n′}
A＝{a j ,j＝1,2,...,w}
wherein X represents a sample set of water-soil resource risk evaluation indexes, and X i The i-th water and soil resource risk evaluation index sample is represented, n' represents the total number of the water and soil resource risk evaluation index samples, A represents the initial clustering center set, and a j Representing the j-th initial cluster center, w representing the total number of the initial cluster centers;
s32, defining the minimum value of the water and soil resource risk evaluation index sample number in each initial clustering center as Q, the minimum distance between any two initial clustering centers as d, the maximum variance as S, the preset clustering center number as R and the maximum iteration number as L;
s33, respectively calculating the distance from each water and soil resource risk evaluation index sample to each initial clustering center, and merging each water and soil resource risk evaluation index sample to a class D corresponding to the initial clustering center with the minimum distance j In (a) and (b);
S34, for category D j If the water and soil resource risk evaluation index sample is smaller than Q, giving up category D j Let the initial cluster center total w=w-1, and let class D j The water and soil resource risk evaluation index samples in the model are re-merged into the categories corresponding to the initial clustering centers with the minimum distance, so that the partitioned clustering centers are obtained;
s35, respectively calculating the distance between any two divided clustering centers when w is smaller than or equal to R/2, so as to obtain a clustering center distance matrix P;
s36, merging two cluster centers corresponding to any cluster center distance element in the cluster center distance matrix P when the cluster center distance element is smaller than d, so as to obtain a merged cluster center;
s37, respectively calculating variances of the water and soil resource risk evaluation index samples in all the categories corresponding to the clustering centers under each dimension when w is greater than or equal to 2R, so as to obtain the maximum variances in the categories;
s38, splitting the clustering center to obtain a first sub-clustering center and a second sub-clustering center after splitting when the maximum variance is greater than S and the number of samples contained in the class is greater than 2 times of the preset minimum number of samples, wherein w=w+1:
D m + ＝D m +θ max
D m - ＝D m -θ max
wherein ,Dm + Represents a first sub-cluster center, D m Representing the cluster center of the split, θ max Represents the maximum variance, D m - Representing a second sub-cluster center;
s39, repeating the steps S33 to S38 until the repetition times reach L, and completing the risk area division of the water and soil resource system network to obtain a divided water and soil resource system network risk area.
5. The method for evaluating the network risk of the basin-soil and water resource system according to claim 4, wherein the calculation expression of the clustering center divided in the step S34 is as follows:
wherein ,aj′ And (3) representing the j' th divided clustering center, and x represents a water-soil resource risk evaluation index sample.
6. The method for evaluating the network risk of the watershed soil and water resource system according to claim 5, wherein the calculation expression of the cluster center after merging in the step S36 is as follows:
wherein E represents the cluster center after combination, n i ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the first cluster center for merging, n j ″ ′ Representing the number of samples of the water and soil resource risk evaluation index in the second cluster center for merging, D i″ Representing the class corresponding to the first cluster center for merging, D j″ Representing the category corresponding to the second cluster center for merging.
7. The method for evaluating the network risk of the basin-soil and water resource system according to claim 6, wherein the step S4 comprises the steps of:
s41, constructing an initial data evaluation matrix based on the average value of the water and soil resource risk evaluation index samples in the classified water and soil resource system network risk area:
wherein Y represents an initial data evaluation matrix, Y pq The q-th watershed water and soil resource risk assessment index of the p-th class is represented;
s42, normalizing water and soil resource risk assessment indexes of each river basin in the initial data assessment matrix:
wherein ,y+ Representing normalizedForward river basin water and soil resource risk assessment index, y - Representing normalized negative drainage basin water and soil resource risk assessment index, y p′ Representing the water and soil resource risk assessment index of the p' th class river basin, y p′min Representing the minimum value, y of the water and soil resource risk assessment index of the p' th class river basin p′max Representing the maximum value of the water and soil resource risk assessment index of the p' type river basin;
s43, respectively carrying out weight calculation on the normalized water and soil resource risk assessment indexes of each river basin based on an entropy weight-TOPSIS evaluation method to obtain the weights of the water and soil resource risk assessment indexes of each river basin:
wherein ,eq″ The weight of the q' th basin water and soil resource risk assessment index is represented, k represents a weight constant coefficient, b p″q″ Representing the duty ratio of the p 'th category in the q' th basin water and soil resource risk assessment index;
s44, based on the weights of the water and soil resource risk assessment indexes of the drainage basins, respectively calculating the entropy weights of the water and soil resource risk assessment indexes of the drainage basins:
wherein ,wq″ Entropy weight of the water and soil resource risk assessment index of the q' th river basin is represented;
s45, obtaining a weight combination vector based on the entropy weight of each river basin water and soil resource risk assessment index:
W＝(w 1 ,w 2 ,...,w q )
wherein W represents a weight combination vector, W q A weight vector representing a water and soil resource risk assessment index of the q-th river basin;
s46, standardized processing of water and soil resource risk assessment indexes of each river basin:
wherein ,Ap″q″ The q 'th basin water and soil resource risk assessment index of the p' th class after standardized treatment is represented;
s47, carrying out standardization processing on the water and soil resource risk assessment indexes and the weight combination vectors of each river basin to obtain a weighted standardization matrix:
z p″q″ ＝w q″ A p″q″ ,z p″q″ ∈Z
wherein Z represents a weighted normalization matrix, w q″ Weight vector z representing q' th basin water and soil resource risk assessment index p″q″ Representing the elements of the p "th row and q" th column in the weighted normalization matrix, A pq The q-th river basin water and soil resource risk assessment index of the p-th class after the standardized treatment is represented;
s48, calculating to obtain the Euclidean distance of the optimal solution and the worst solution of each water and soil resource evaluation object based on the weighted standardized matrix:
wherein ,
euclidean distance representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Euclidean distance representing the worst solution of the q' th water and soil resource evaluation object, < ->
Representing the optimal solution of the q' th water and soil resource evaluation object,/for>
Represents the worst solution of the q' th water and soil resource evaluation object S p″q″ Solution representing p-th "q-th" water and soil resource evaluation object, ++>
Representing the optimal solution of the q-th water and soil resource evaluation object>
Representing the worst solution of the q-th water and soil resource evaluation object;
s49, constructing the relative proximity corresponding to the partitioned water and soil resource system network risk areas based on the Euclidean distance of the optimal solution and the worst solution of each water and soil resource evaluation object, and sorting the relative proximity from big to small to obtain a basin water and soil resource system network risk evaluation result with risk levels corresponding to the sorting from small to big.
8. The method for evaluating the network risk of the basin-soil and water resource system according to claim 7, wherein the calculation expression of the relative proximity in the step S49 is as follows:
wherein ,
representing relative proximity. />
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