CN106339536B - Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model - Google Patents
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Abstract
A kind of Comprehensive Evaluation of Water Quality based on water pollution index's method and cloud model is to improve water pollution index's method using cloud model on the basis of water pollution index's method.Cloud model is combined with the WPI value in water pollution index's method, WPI value evaluation cloud model is obtained by cloud parameter determination method, single-factor WPI value cloud model is obtained by backward cloud generator, WPI value evaluation cloud atlas and single-factor WPI value cloud atlas are generated with normal state cloud generator, intuitively finds out the pollution situation of each single-factor；In conjunction with weight, comprehensive WPI value cloud model is obtained by comprehensive cloud algorithm, and showed with water dust formal intuition, determine Water Quality Evaluation grade, effectively solves the problems, such as not considering whole pollution factors in the contribution in comprehensive water quality assessment and the comparison being difficult to realize between different section in water pollution index's method.
Description
Technical Field
The invention belongs to the technical field of information processing, and relates to a water quality comprehensive evaluation method based on a water pollution index method and a cloud model.
Background
The water environment system is a complex system integrating ambiguity and randomness, and in order to ensure the rationality of a water quality evaluation result, the ambiguity and the randomness existing in the evaluation process must be comprehensively considered.
The prior documents are as follows: the Chinese environmental monitoring, 2013, 03, discloses an application study of a water pollution index method in river water quality evaluation, and the article discloses an evaluation idea of the water pollution index method:
based on the evaluation principle of a single factor evaluation method, according to a water quality category and WPI (water pollution index value) corresponding table (see table 3), the WPI of each participating water quality evaluation item of a certain section is calculated by an interpolation method, and the highest WPI is taken as the WPI of the section.
TABLE 3 water quality type and WPI value correspondence table
| Class of water quality | Class I | Class II | Class III | Class IV | Class V | Poor class V |
| WPI range | WPI＝20 | 20<WPI≤40 | 40<WPI≤60 | 60<WPI≤80 | 80<WPI≤100 | WPI>100 |
The water pollution index method comprises the following calculation steps:
(1) the WPI value calculation method for the index when the water pollution index does not exceed the class V water limit value is as follows:
Cl(i)＜C(i)≤Ch(i)
wherein C (i) is the actual monitoring value of the ith water quality index, Cl(i)、Ch(i) Respectively the lower limit value and the upper limit value of the classification standard of the ith water quality index, WPIl(i)、WPIh(i) The index values corresponding to the lower limit value and the upper limit value of the classification standard of the ith water quality index, and WPI (i) is the index value corresponding to the ith water quality index.
Furthermore, when 6< pH <9,
WPI(pH)＝20
(2) and when the water limit value of the class V is exceeded, the WPI value calculation method comprises the following steps:
in the formula, C5(i) Is the standard concentration limit value of the class V in the water quality category of the ith project.
Furthermore, when the pH is <6,
WPI(pH)＝100+6.67×(6-pH)
when the pH is greater than 9, the pH is adjusted,
WPI(pH)＝100+8.00×(pH-9)
(3) determination of comprehensive water quality WPI value
WPI＝MAX(WPI(i))
Compared with the single-factor evaluation method, the water pollution index method continues the idea that the single-factor evaluation method uses the most serious pollution index as the water quality type judgment method, but can quantify the water quality condition. According to the quantification result, the water quality type can be visually judged, and the time-space change condition of the water quality can be reflected. According to the application comparison condition of the water pollution index method and other 4 water quality evaluation methods, the water pollution index method can simultaneously meet the requirements of quantitative water quality evaluation, main pollution index identification, water quality category evaluation and poor V-class water quality comparison, and the comparison result is shown in Table 4.
TABLE 45 comparison of water quality evaluation methods
In summary, the prior art has the following disadvantages:
(1) in the evaluation process, the pollution heaviest factor is given 100% weight, the difference of the influence degree of a plurality of different pollution indexes on the water quality is not considered, and the water quality monitoring information is not fully utilized.
(2) The WPI value of the heaviest pollution index is taken as the WPI value of the section, and when the main pollution indexes are different, the WPI values of different sections are not very strong in comparability.
(3) In the aspect of water quality category evaluation, the ambiguity and uncertainty objectively existing in the water environment are not considered, and the water quality evaluation result is inferior to the water quality category obtained by the comprehensive evaluation method.
Disclosure of Invention
The invention aims to provide a water quality comprehensive evaluation method based on a water pollution index method and a cloud model, wherein the cloud model is combined with a WPI value in the water pollution index method, the WPI value evaluation cloud model is obtained by a cloud parameter determination method, a single-factor WPI value cloud model is obtained by a reverse cloud generator, a WPI value evaluation cloud picture and a single-factor WPI value cloud picture are generated by a normal cloud generator, and the pollution condition of each single factor is visually seen; and combining the weight, obtaining a comprehensive WPI value cloud model by a comprehensive cloud algorithm, visually displaying in a cloud drop mode, judging the comprehensive water quality evaluation grade, and effectively solving the problems that the contribution of all pollution factors in comprehensive water quality evaluation is not considered in a water pollution index method and comparison among different sections is difficult to realize.
The technical scheme adopted by the invention is that a water quality comprehensive evaluation method based on a water pollution index method and a cloud model is carried out according to the following steps:
step 1, determining an evaluated factor set, a WPI value set and a weight set;
step 2, establishing a WPI value evaluation cloud model;
step 3, obtaining a single-factor WPI value cloud model;
step 4, obtaining a comprehensive WPI value cloud model;
and 5, determining the water quality evaluation grade.
Further, the step 1 is performed according to the following steps:
step a, selecting an evaluation index and determining a factor set;
firstly, selecting an initial index; then, a principal component-correlation analysis method is adopted for index screening, redundant indexes are deleted by using principal component analysis, and repeatability indexes are deleted by using correlation analysis, so that the condition that the indexes are kept to have obvious influence on the evaluation result and the condition that the information overlapping degree between the indexes is low is ensured; the finally screened evaluation indexes are used as a factor set;
the selection of the initial index is determined according to an evaluation object and can be carried out through a water quality monitoring report or the existing evaluation index;
the basic model of principal component analysis is:
in the formula, xiIndicates the ith index (i ═ 1,2, …, p); z is a radical ofjRepresents the jth principal component (j ═ 1,2, …, m); lijRepresenting the principal component load corresponding to the ith index in the jth principal component; p represents the number of indexes; m represents the number of principal components;
the principal component analysis steps are as follows:
1: calculating a correlation coefficient matrix R of the index standardized data;
in the formula, rijIs the correlation coefficient, x, of the ith and jth indiceskiAnd xkjThe values of the ith and jth indexes of the kth evaluation object respectively,andthe average values of the i-th index and the j-th index are respectively;
2: calculating the eigenvalue lambda of the correlation coefficient matrix RiAnd a feature vector ei(i ═ 1,2, …, p), variance contribution ratio ωiAnd the cumulative contribution rate G (m);
λirepresenting the total variance of the original index data interpreted by the ith principal component, the variance contribution rate omega of the ith principal component to the original index dataiComprises the following steps:
the cumulative contribution rate G (m) is
3: selecting principal components according to the characteristic values or the accumulated contribution rates, and determining the number m of the principal components;
the principle component selection criterion is as follows: (1) taking the eigenvalue lambdaiMore than 1 corresponding main component; (2) taking the main component corresponding to the cumulative contribution rate G (k) being more than or equal to 85 percent;
4: calculating principal component factor load lij；
Let eijFeature vector e as the ith indexiThe factor load is calculated as
5: screening indexes according to the absolute value of the factor load on the main component;
the larger the absolute value of the factor load is, the more obvious the influence of the index on the evaluation result is, and the more the factor load is to be kept; the smaller the absolute value of the factor load is, the weaker the influence of the index on the evaluation result is, and the more the index is to be removed;
the specific steps of the correlation analysis are as follows:
1: calculating correlation coefficients among the evaluation indexes;
let rijIs the correlation coefficient, x, of the ith and jth indiceskiAnd xkjThe values of the ith and jth indexes of the kth evaluation object respectively,andthe average values of the i-th index and the j-th index are respectively;
2: giving a critical value P (P is more than 0 and less than 1), and judging the removal of the index;
when rijIf the absolute value is less than P, two evaluation indexes are simultaneously reserved; when rijIf the influence meanings of the two indexes are similar when the value is greater than P, one evaluation index can be deleted according to judgment of importance, and if the influence meanings of the indexes are different greatly, the two indexes are both deletedReserving;
b, determining a comment set, namely a WPI value set;
determining a comment set of water quality evaluation by combining with the existing water quality evaluation standard, giving out a WPI value range corresponding to each evaluation grade in the comment set, and determining the WPI value set;
step c, determining a weight set;
determining weight by a combined weighting method based on AHP-CRITIC, determining subjective weight by using an AHP method, determining objective weight by using a CRITIC method, obtaining combined weight of evaluation indexes by using a combined weight determination formula, and determining a weight set;
the specific steps of determining the weight by the AHP method are as follows:
1: determining a water quality evaluation index;
2: constructing a judgment matrix;
the element values in the judgment matrix are quantitative indexes for judging the relative importance of each element, and a 1-9 scale method is generally adopted; the numerical value of each factor in the judgment matrix is obtained by judging the relative importance degree of each factor by people and then quantifying the judgment according to a certain ratio scale;
3: calculating the maximum eigenvalue and eigenvector of the judgment matrix, and determining a weight vector;
at present, there are many methods for calculating characteristic values and characteristic vectors, and a square root method, a sum method, a characteristic root method, a least square method and the like are commonly used;
4: checking the consistency;
in order to ensure the reasonability of the conclusion, the consistency check of the judgment matrix is required, and the steps are as follows:
(1) calculating a consistency check index CI;
let λmaxJudging the maximum eigenvalue of the matrix, then
(2) Searching for a corresponding average random consistency index RI according to the table 1, wherein n represents the order of a judgment matrix;
TABLE 1 average random consistency index
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 |
(3) Calculating a random consistency ratio CR according to the formula
When CR is less than 0.10, the judgment matrix is considered to have satisfactory consistency, otherwise, the judgment matrix needs to be adjusted to have satisfactory consistency;
the specific steps of determining the weight by the CRITIC method are as follows:
1: calculating the standard deviation of the index sample;
let sigmajThe standard deviation of the jth index is calculated by the following formula:
wherein N is the number of samples, xiFor the sample values, the values of the samples,is the sample mean;
step 2: computing
And step 3: calculating the information content contained in the index;
let CjThe calculation formula of the information amount contained in the j index is as follows:
Cjthe larger the information content contained in the jth index is, the greater the relative importance of the index is;
4: calculating objective weight of the index;
let omegajThe objective weight of the jth index is calculated by the formula:
the combination weight determination formula is as follows:
in the formula, WjIs the combined weight of the j-th index,the subjective weight of the jth index obtained by the AHP method,the objective weight of the jth index obtained by the CRITIC method is used.
Further, the step 2 is performed according to the following steps:
step a, determining cloud parameters;
the cloud model integrally represents a qualitative concept by using 3 digital features of expected Ex, entropy En and super-entropy He;
it is expected that Ex: the expectation of the cloud droplets in the domain space is the central value of the concept in the domain space, is the point which can represent the qualitative concept most, and has the membership degree of 1, namely 100 percent of membership to the qualitative concept;
entropy En: is a measure of uncertainty of a qualitative concept, which is determined by randomness and ambiguity of the qualitative concept; en is a measure of randomness of a qualitative concept, reflecting the degree of dispersion of cloud droplets that can represent the qualitative concept; meanwhile, En also reflects the abundance of the qualitative concept which is also the same, reflects the value range of the cloud drops which can be accepted by the qualitative concept in the discourse space, and is the measurement of the ambiguity of the qualitative concept; the larger the En is, the larger the value range of the cloud droplets accepted by the qualitative concept is, and the fuzzy the qualitative concept is; the randomness and the ambiguity are reflected by the same number space characteristic, and the relevance between the randomness and the ambiguity is necessarily reflected;
hyper-entropy He: the method is a measure of uncertainty of entropy, namely entropy of the entropy is jointly determined by randomness and ambiguity of the entropy, and reflects the cohesiveness of uncertainty of all points representing the language value in a domain space, and the size of the cohesiveness can represent the dispersion and thickness of cloud;
adopting bilateral constraint [ C ] when calculating digital characteristics of comment cloudmin，Cmax]To determine the cloud parameters, the calculation formula is as follows:
Ex＝(Cmin+Cmax)/2
En＝(Cmax-Cmin)/6
He＝k
k is a constant set according to the condition of the comment, and the fuzzy degree of the comment is reflected; [ C ]min，Cmax]Representing the WPI value range corresponding to each evaluation grade in the comment set;
when only one-sided constraint comment exists, the cloud parameters are determined by combining the upper limit and the lower limit of the data, the parameters of the default boundary are determined, and then calculation is carried out according to the formula; on the basis of the formula, the obtained cloud parameter determination method is as follows:
1) cloud parameters corresponding to evaluation interval 1(0, a):
Ex1＝0
En1＝a/3
He1＝k
2) evaluation section i (C)min，Cmax) (0 < i < n) corresponding cloud parameters:
Exi＝(Cmin+Cmax)/2
Eni＝(Cmax-Cmin)/6
Hei＝k
3) cloud parameters corresponding to the evaluation interval n (m, + ∞):
Ex＝Cmin′+Cmax′
En＝Cmin′/3
He＝k
Cmin′、Cmax' upper and lower limits of the evaluation interval n-1, respectively;
taking the evaluation indexes having six evaluation intervals (0, a ], (a, b ], (b, c ], (c, d ], (d, e ], [ e, + ∞) as examples, the determination of the evaluation cloud model parameters is shown in table 2;
TABLE 2 determination of cloud model parameters (Ex, En, He)
| Cloud | Ex | En | He |
| C1 | Ex1＝0 | En1＝a/3 | k |
| C2 | Ex2＝(a+b)/2 | En2＝(b-a)/6 | k |
| C3 | Ex3＝(b+c)/2 | En3＝(c-b)/6 | k |
| C4 | Ex4＝(c+d)/2 | En4＝(d-c)/6 | k |
| C5 | Ex5＝(d+e)/2 | En5＝(e-d)/6 | k |
| C6 | Ex6＝d+e | En6＝d/3 | k |
Step b, generating a cloud model;
evaluating cloud model parameters Ex, En and He according to the determined WPI value, and generating a corresponding WPI value evaluation cloud chart by using a normal cloud generator;
the specific process is as follows:
inputting: cloud model digital features (Ex, En, He) and the number n of generated cloud droplets;
and (3) outputting: quantitative data x of n cloud dropletsiAnd degree of certainty y of its corresponding qualitative concepti(i＝1,2,…,n)；
The algorithm comprises the following steps:
1: generating the desired value, He, of En2A normal random number Enn that is the variance;
2: generation of expected value of Ex, Enn2A normal random number x being the varianceiI.e. xiThe method is a specific quantitative realization of a qualitative concept A on a corresponding quantitative discourse domain, and is called cloud drop quantitative data;
3: computing
Definition of yiIs xiDegree of certainty belonging to qualitative concept A, (x)i,yi) Is cloud drop;
4: repeating the above steps until n cloud droplets (x) are generatedi,yi) (i ═ n) up to.
Further, the step 3 is performed according to the following steps:
step a, calculating WPI values corresponding to water quality evaluation factors based on a water pollution index method;
the water pollution index method is based on the evaluation principle of a single-factor evaluation method, and according to the water quality category and a WPI value corresponding table (see table 3), the WPI value of each participating water quality evaluation item of a certain section is calculated by an interpolation method, and the highest WPI value is taken as the WPI value of the section;
TABLE 3 water quality type and WPI value correspondence table
| Class of water quality | Class I | Class II | Class III | Class IV | Class V | Poor class V |
| WPI range | WPI＝20 | 20<WPI≤40 | 40<WPI≤60 | 60<WPI≤80 | 80<WPI≤100 | WPI>100 |
The WPI value calculation formula of each water quality evaluation factor is as follows:
1) the WPI value calculation method for the index when the water pollution index does not exceed the class V water limit value is as follows:
Cl(i)＜C(i)≤Ch(i)
wherein C (i) is the actual monitoring value of the ith water quality index, Cl(i)、Ch(i) Respectively the lower limit value and the upper limit value of the classification standard of the ith water quality index, WPIl(i)、WPIh(i) The index values are respectively corresponding to the lower limit value and the upper limit value of the classification standard of the ith water quality index, and WPI (i) is the index value corresponding to the ith water quality index;
furthermore, when 6< pH <9,
WPI(pH)＝20
WIP (pH) is the WPI value corresponding to the index pH value;
2) and when the water limit value of the class V is exceeded, the WPI value calculation method comprises the following steps:
in the formula, C5(i) Is the standard concentration limit value of the class V in the water quality category of the ith project;
furthermore, when the pH is <6,
WPI(pH)＝100+6.67×(6-pH)
when the pH is greater than 9, the pH is adjusted,
WPI(pH)＝100+8.00×(pH-9)
step b, generating a single-factor WPI value cloud model;
computing parameters of a cloud model using an inverse cloud algorithm based on X information, using cloud droplet X (X) onlyi) To reduce the three parameters of the cloud without the need for a degree of certainty Y (Y)i) The procedure is as follows:
inputting: sample point xi(i＝1,2,…,n)；
And (3) outputting: the n cloud droplets correspond to the cloud model numerical features (Ex, En, He) of the qualitative concept;
the algorithm comprises the following steps:
1: according to xiCalculating the mean of the samples for the set of dataAbsolute center distance of first order sampleSample variance
2: calculating an expectation
3: computing entropy
4: computing hyper-entropy
Cloud drop x in algorithmiRefer to what is collected in actual applicationAnd (4) WPI value corresponding to the actual monitoring data of the water quality evaluation index.
Further, the step 4 is performed according to the following steps: and (3) synthesizing the cloud models of the single-factor WPI values by applying a comprehensive cloud algorithm and combining weights to generate a comprehensive WPI value cloud model, wherein the comprehensive cloud algorithm formula is as follows:
further, the step 5 is performed according to the following steps: and generating a WPI value evaluation cloud picture and a comprehensive WPI value cloud picture by using a normal cloud generator, comparing, and finally determining the comprehensive water quality evaluation grade.
The method has the advantages that the evaluation language is subjected to fuzzy processing, indexes with randomness and fuzziness characteristics are quantified, the fuzziness and the randomness of the water quality evaluation factors are represented through 3 digital characteristics of the cloud model, the pollution state of each single factor can be visually seen, the comprehensive water quality grade of seepage water is visually shown in a cloud drop mode, the evaluation result is more practical, the defects and the defects of a water pollution index method can be effectively overcome, and a more optimized model or a new idea is provided for the research of a water quality evaluation method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method for comprehensively evaluating water quality in the present invention.
Figure 2 is a cloud of WPI value evaluations.
Figure 3 is a cloud graph of single factor WPI values.
Wherein FIG. 3a is a WPI value cloud model for pH, FIG. 3b is a WPI value cloud model for U, FIG. 3c is a WPI value cloud model for Ra, FIG. 3d is a WPI value cloud model for Σ β, and FIG. 3e is NH4-a WPI value cloud model of N; FIG. 3f is a WPI value cloud model for Mn; FIG. 3g is a WPI value cloud model for F-ion; figure 3h is a WPI value cloud model for Zn.
Figure 4 is a comprehensive WPI value cloud.
Wherein FIG. 4a is a cloud chart of comprehensive WPI values of dam section A water seepage; FIG. 4B is a cloud chart of comprehensive WPI values of water seepage of a dam section B; FIG. 4C is a cloud chart of comprehensive WPI values of C-seepage water of a dam section; figure 4D is a cloud chart of comprehensive WPI values of water seepage of dam section D.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A water quality comprehensive evaluation method based on a water pollution index method and a cloud model specifically comprises the following steps:
taking the evaluation of the water seepage quality of the retired uranium tailing pond as an example, on the basis of a given evaluation index system and index weight of the water seepage quality of the retired uranium tailing pond, the comprehensive evaluation of the water quality is carried out by using a cloud model improved water pollution index method, and the specific steps are shown in fig. 1.
1) Determination of factor set, comment set and weight set
According to the existing environment monitoring report of the retired uranium tailing pond, 12 primary selection indexes of the evaluation of the water seepage quality of the retired uranium tailing pond are determined, namely, the indexes comprise pH, U, Ra, sigma α, sigma β,230Th、210Po、210Pb、NH4-N、Mn、F-Zn, screening indexes based on a principal component-correlation analysis method, and determining the final evaluation indexes of the seepage water quality as pH, U, Ra, sigma β and NH4-N、Mn、F-、Zn。
Based on an AHP-CRITIC combined weighting method, subjective weights are determined to be (0.0605,0.2822,0.1760,0.1760,0.049,0.0605,0.0386 and 0.1032) by using the AHP method, objective weights are determined to be (0.1088,0.0783,0.1049,0.1808,0.0998,0.1731,0.0910 and 0.1632) by using the CRITIC method, and the subjective weights and the objective weights are substituted into a combined weight determination formula to determine a weight set to be (0.0534, 0.1793, 0.1498, 0.2582, 0.049, 0.1450, 0.0285 and 0.1369).
Combining GB8978 comprehensive wastewater discharge standard, dividing the water quality of the seepage water into six grades on the basis of the existing water quality pollution index grading standard (see Table 5), wherein the evaluation sets are { "clean", "light pollution", "moderate pollution", "heavy pollution", "severe pollution" }, and are respectively marked as I, II, III, IV, V and VI; according to the WPI value evaluation range in the existing water pollution index method (see table 3), the WPI value set is determined to be { [0, 20], (20, 40], (40, 60], (60, 80], (80, 100], (100, + ∞) }, and the finally determined retired uranium tailing pond seepage water quality evaluation standard is shown in table 6.
TABLE 5 existing water pollution index grading Standard
| Index of water quality | Rank of | Basis of classification |
| <0.2 | Cleaning of | Undetected for multiple items |
| 0.2～0.4 | Cleaning and cleaning | The detected values are all within the standard values |
| 0.4～0.7 | Slight pollution | 1 item detected value exceeds the standard |
| 0.7～1.0 | Moderate pollution | 2 detected value exceeds the standard |
| 1.0～2.0 | Severe pollution | All or most of the items are out of standard |
| >2.0 | Severe pollution | All or most of the items are out of standard>1 times of |
TABLE 6 classification standard of water seepage quality of retired uranium tailings ponds
2) Establishment of WPI value evaluation cloud model
(1) Determination of cloud parameters
Adopting bilateral constraint [ C ] when calculating digital characteristics of comment cloudmin，Cmax]To determine the cloud parameters, the calculation formula is as follows:
Ex＝(Cmin+Cmax)/2
En＝(Cmax-Cmin)/6
He＝k
k is a constant set according to the condition of the comment, and the fuzzy degree of the comment is reflected; [ C ]min，Cmax]Representing the WPI value range corresponding to each evaluation grade in the comment set;
when only one-sided constraint comment exists, the cloud parameters are determined by combining the upper limit and the lower limit of the data, the parameters of the default boundary are determined, and then calculation is carried out according to the formula; on the basis of the formula, the obtained cloud parameter determination method is as follows:
1) cloud parameters corresponding to evaluation interval 1(0, a):
Ex1＝0
En1＝a/3
He1＝k
2) evaluation section i (C)min，Cmax) (0 < i < n) corresponding cloud parameters:
Exi＝(Cmin+Cmax)/2
Eni＝(Cmax-Cmin)/6
Hei＝k
3) cloud parameters corresponding to the evaluation interval n (m, + ∞):
Ex＝Cmin′+Cmax′
En＝Cmin′/3
He＝k
Cmin′、Cmax' the upper and lower limits of the evaluation interval n-1 are indicated, respectively.
Taking the evaluation indexes having six evaluation intervals (0, a ], (a, b ], (b, c ], (c, d ], (d, e ], [ e, + ∞) as examples, the determination of the evaluation cloud model parameters is shown in table 2.
TABLE 2 determination of cloud model parameters (Ex, En, He)
According to the water quality classification standard in table 5, the WPI values were determined by the method in table 2 to evaluate the parameters of the cloud model, which are (0,6.67,1), (30,3.33,1), (50,3.33,1), (70,3.33,1), (90,3.33,1), (180,26.67,1), respectively.
(2) Generation of WPI value evaluation cloud model
And evaluating cloud model parameters Ex, En and He according to the determined WPI value, and generating a corresponding WPI value evaluation cloud picture by using a normal cloud generator. The process is as follows:
inputting: cloud model digital features (Ex, En, He) and the number of generated cloud droplets n.
Output of: quantitative data x of n cloud dropletsiAnd degree of certainty y of its corresponding qualitative concepti(i＝1,2,…,n)。
The algorithm comprises the following steps:
1: generating the desired value, He, of En2A normal random number Enn that is the variance;
2: generation of expected value of Ex, Enn2A normal random number x being the varianceiI.e. xiThe method is a specific quantitative realization of a qualitative concept A on a corresponding quantitative discourse domain, and is called cloud drop quantitative data;
3: computing
4: definition of yiIs xiDegree of certainty belonging to qualitative concept A, (x)i,yi) Is cloud drop;
5: repeating the steps until n cloud drops are generated.
WPI value evaluation cloud images generated by a normal cloud generator are shown in fig. 2.
3) Cloud model for obtaining single-factor WPI value
(1) Calculation of WPI value corresponding to each Water quality evaluation factor
And obtaining the WPI value corresponding to each water quality evaluation factor by applying a WPI value calculation formula according to the actual measurement data of each water quality evaluation factor.
According to index monitoring data of water seepage of four dam sections in 2012 (1-12 months), a WPI value calculation formula is used to obtain a WPI value corresponding to each index of each dam section, and a WPI value of a water seepage index of a dam section A is given in a table 7.
Table 72012 WPI value of dam section A water seepage index
(2) Single factor WPI value cloud model generation
The parameters of the cloud model are calculated by using a reverse cloud new algorithm based on X information, only three parameters of the cloud are reduced by using the quantitative value of the cloud droplet X, the value of the degree Y does not need to be determined, and the process is as follows:
inputting: sample point xi(i＝1,2,…,n)。
And (3) outputting: these n cloud droplets correspond to the cloud model numerical features (Ex, En, He) of the qualitative concept.
The algorithm comprises the following steps:
1: according to xiCalculating the mean of the samples for the set of dataAbsolute center distance of first order sampleSample variance
2: calculating an expectation
3: computing entropy
4: computing hyper-entropy
Cloud drop x in algorithmiThe WPI value is the WPI value corresponding to the water quality evaluation index actual monitoring data collected in the actual application.
According to the WPI value of the water seepage index, parameters of a single-factor WPI value cloud model corresponding to the 8 indexes are calculated according to a reverse cloud new algorithm based on X information, and the parameters are shown in a table 8.
TABLE 8 Single-factor WPI value cloud model
Taking the dam section a as an example, a normal cloud generator is used to generate a WPI value cloud chart corresponding to 8 single factors according to the obtained single factor WPI value cloud model, and the WPI value cloud chart is compared with the WPI value evaluation cloud chart, as shown in fig. 3. In the figure, gray is a single-factor WPI value cloud model, and pure black is a WPI value evaluation cloud model.
4) Determination of comprehensive WPI value cloud model
The comprehensive cloud algorithm formula is as follows:
and substituting the determined single-factor WPI value cloud model into a comprehensive cloud operation formula by combining the weight of the water seepage index to obtain a comprehensive WPI value cloud model of water seepage of each dam section, see table 9, and generating a comprehensive WPI value cloud map of water seepage of each dam section by using a normal cloud generator, wherein red is the comprehensive WPI value cloud map of water seepage of each dam section, and blue is the WPI value evaluation cloud map, as shown in fig. 4.
TABLE 9 comprehensive WPI value cloud model
| Dam section | Comprehensive WPI value cloud model |
| Dam section A | (62.6947,5.5986,4.2850) |
| Dam section B | (130.6272,19.0690,14.8482) |
| Dam section C | (69.1420,7.2551,1.0598) |
| Dam section D | (151.1792,21.3879,0.8707) |
5) Comprehensive evaluation results and analysis
The result of determining the water quality evaluation grade of the seepage water of each dam section is shown in a table 10 by combining the WPI value cloud chart and the WPI value evaluation cloud chart.
TABLE 10 comprehensive evaluation results
| Dam section | Comprehensive WPI value cloud model | Evaluation results | Main pollutant |
| Dam section A | (62.6947,5.5986,4.2850) | Ⅳ | Mn(119)、pH(108) |
| Dam section B | (130.6272,19.0690,14.8482) | Ⅵ | Mn(229)、pH(110) |
| Dam section C | (69.1420,7.2551,1.0598) | Ⅳ | NH4-N(158)、Mn(112) |
| Dam section D | (151.1792,21.3879,0.8707) | Ⅵ | Mn(248)、pH(102)、NH4-N(81) |
And (4) analyzing results:
(1) single factor pollution grade in dam section A water seepage quality evaluation can be visually seen from figure 3, wherein the pollution grade of pH and Mn is 'serious pollution', the pollution grade of U, Ra, sigma β, F ions and Zn is 'clean', and the pollution grade of NH is4The contamination rating of-N is "clean", so the main indicators of contamination in the dam section A permeate can be determined as pH and Mn. Meanwhile, normal cloud is used for expressing single-factor pollution levels, comparison between pollution levels of different pollution indexes of the same level can be carried out, the pollution levels of indexes of pH and Mn in the seepage water of the dam section A are 'serious pollution', the WPI value expectation and cloud drop dispersion degree are compared, Mn is obviously higher than pH, and the pollution level of pH is lower than Mn.
(2) Comprehensive evaluation grade of water quality: as can be seen from Table 9, the water quality grades of the seepage water of the dam section A and the seepage water of the dam section C are 'moderate pollution', and the water quality grades of the seepage water of the dam section B and the seepage water of the dam section D are 'severe pollution'. As can be seen from fig. 4, the comprehensive WPI value cloud models of the water seepage of the dam section a and the water seepage of the dam section C are both located between two clouds of "light pollution" and "moderate pollution", and the comprehensive WPI value expectation of the water seepage of the dam section C is almost completely close to the WPI value evaluation expectation of "moderate pollution", and is obviously higher than the comprehensive WPI value cloud model of the water seepage of the dam section a; the comprehensive WPI value cloud models of the water seepage of the dam section B and the water seepage of the dam section D are located between two clouds of 'severe pollution' and 'severe pollution', the comprehensive WPI value expectation of the water seepage of the dam section B is lower than the comprehensive WPI value expectation of the water seepage of the dam section D, and the cloud drop dispersion degree of the water seepage of the dam section B is far higher than the cloud drop dispersion degree of the water seepage of the dam section D. The comprehensive comparison results in that: the water quality pollution degree of the water seepage of the dam section B is the highest, the water seepage of the dam section D and the water seepage of the dam section C are performed, and the water quality pollution degree of the water seepage of the dam section A is the lowest.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (5)
1. A water quality comprehensive evaluation method based on a water pollution index method and a cloud model is characterized by comprising the following steps:
step 1, determining an evaluated factor set, a WPI value set and a weight set;
step 2, establishing a WPI value evaluation cloud model;
step 3, obtaining a single-factor WPI value cloud model;
step 4, obtaining a comprehensive WPI value cloud model;
step 5, determining a water quality evaluation grade;
the step 1 is carried out according to the following steps:
step a, selecting an evaluation index and determining a factor set;
firstly, selecting an initial index; then, screening indexes by adopting a principal component-correlation analysis method, deleting redundant indexes by adopting principal component analysis, deleting repeatability indexes by adopting correlation analysis, and taking the finally screened evaluation indexes as a factor set;
the selection of the initial index is determined according to an evaluation object, and the initial index is selected through a water quality monitoring report or the existing evaluation index;
the basic model of principal component analysis is:
in the formula, xiIndicates the ith index (i ═ 1,2, …, p); z is a radical ofjRepresents the jth principal component (j ═ 1,2, …, m); lijRepresenting the principal component load corresponding to the ith index in the jth principal component; p represents the number of indexes; m represents the number of principal components;
the principal component analysis steps are as follows:
1: calculating a correlation coefficient matrix R of the index standardized data;
in the formula, rijIs the correlation coefficient, x, of the ith and jth indiceskiAnd xkjThe values of the ith and jth indexes of the kth evaluation object respectively,andthe average values of the i-th index and the j-th index are respectively;
2: calculating the eigenvalue lambda of the correlation coefficient matrix RiAnd a feature vector ei(i ═ 1,2, …, p), variance contribution ratio ωiAnd the cumulative contribution rate G (m);
λirepresenting the total variance of the original index data interpreted by the ith principal component, the variance contribution rate omega of the ith principal component to the original index dataiComprises the following steps:
the cumulative contribution rate G (m) is
3: selecting principal components according to the characteristic values or the accumulated contribution rates, and determining the number m of the principal components;
the principle component selection criterion is as follows: (1) taking the eigenvalue lambdaiMore than 1 corresponding main component; (2) taking the main component corresponding to the cumulative contribution rate G (k) being more than or equal to 85 percent;
4: calculating principal component factor load lij；
Let eijFeature vector e as the ith indexiThe factor load is calculated as
5: screening indexes according to the absolute value of the factor load on the main component;
the larger the absolute value of the factor load is, the more obvious the influence of the index on the evaluation result is, and the more the factor load is to be kept; the smaller the absolute value of the factor load is, the weaker the influence of the index on the evaluation result is, and the more the index is to be removed;
the specific steps of the correlation analysis are as follows:
1: calculating correlation coefficients among the evaluation indexes;
let rijIs the correlation coefficient, x, of the ith and jth indiceskiAnd xkjThe values of the ith and jth indexes of the kth evaluation object respectively,andthe average values of the i-th index and the j-th index are respectively;
2: giving a critical value P (P is more than 0 and less than 1), and judging the removal of the index;
when rijIf the absolute value is less than P, two evaluation indexes are simultaneously reserved; when rijIf the influence meanings of the two indexes are similar when the value is greater than P, one evaluation index can be deleted according to the judgment of importance, and if the influence meanings of the indexes are different greatly, the two indexes are reserved;
b, determining a comment set, namely a WPI value set;
determining a comment set of water quality evaluation by combining with the existing water quality evaluation standard, giving out a WPI value range corresponding to each evaluation grade in the comment set, and determining the WPI value set;
step c, determining a weight set;
determining weight by a combined weighting method based on AHP-CRITIC, determining subjective weight by using an AHP method, determining objective weight by using a CRITIC method, obtaining combined weight of evaluation indexes by using a combined weight determination formula, and determining a weight set;
the AHP method comprises the following specific steps of:
1: determining a water quality evaluation index;
2: constructing a judgment matrix;
the element values in the judgment matrix are quantitative indexes for judging the relative importance of each element, and a 1-9 scale method is adopted; the numerical value of each factor in the judgment matrix is obtained by judging the relative importance degree of each factor by people and then quantifying the judgment according to the ratio scale;
3: calculating the maximum eigenvalue and eigenvector of the judgment matrix, and determining a weight vector;
4: checking the consistency;
and (3) carrying out consistency check on the judgment matrix, wherein the steps are as follows:
(1) calculating a consistency check index CI;
let λmaxJudging the maximum eigenvalue of the matrix, then
(2) Searching for a corresponding average random consistency index RI according to the table 1, wherein n represents the order of a judgment matrix;
TABLE 1 average random consistency index
(3) Calculating a random consistency ratio CR according to the formula
When CR is less than 0.10, the judgment matrix is considered to have satisfactory consistency, otherwise, the judgment matrix needs to be adjusted to have satisfactory consistency;
the specific steps of determining the weight by the CRITIC method are as follows:
1: calculating the standard deviation of the index sample;
let sigmajThe standard deviation of the jth index is calculated by the following formula:
wherein N is the number of samples, xiFor the sample values, the values of the samples,is the sample mean;
step 2: computing
And step 3: calculating the information content contained in the index;
let CjThe calculation formula of the information amount contained in the j index is as follows:
Cjthe larger the information content contained in the jth index is, the greater the relative importance of the index is;
4: calculating objective weight of the index;
let omegajThe objective weight of the jth index is calculated by the formula:
the combination weight determination formula is as follows:
in the formula, WjIs the combined weight of the j-th index,the subjective weight of the j index obtained by the AHP method，The objective weight of the jth index obtained by the CRITIC method is used.
2. The comprehensive water quality evaluation method based on the water pollution index method and the cloud model according to claim 1, wherein the step 2 is performed according to the following steps:
step a, determining cloud parameters;
adopting bilateral constraint [ C ] when calculating digital characteristics of comment cloudmin，Cmax]To determine the cloud parameters, the calculation formula is as follows:
Ex＝(Cmin+Cmax)/2
En＝(Cmax-Cmin)/6
He＝k
wherein,
it is expected that Ex: the expectation of the cloud droplets in the domain space is the central value of the concept in the domain space, is the point which can represent the qualitative concept most, and has the membership degree of 1, namely 100 percent of membership to the qualitative concept;
entropy En: is a measure of uncertainty of a qualitative concept, which is determined by randomness and ambiguity of the qualitative concept; en is a measure of randomness of a qualitative concept, reflecting the degree of dispersion of cloud droplets that can represent the qualitative concept; meanwhile, En also reflects the abundance of the qualitative concept which is also the same, reflects the value range of the cloud drops which can be accepted by the qualitative concept in the discourse space, and is the measurement of the ambiguity of the qualitative concept; the larger the En is, the larger the value range of the cloud droplets accepted by the qualitative concept is, and the fuzzy the qualitative concept is; the randomness and the ambiguity are reflected by the same number space characteristic, and the relevance between the randomness and the ambiguity is necessarily reflected;
hyper-entropy He: the method is a measure of uncertainty of entropy, namely entropy of the entropy is jointly determined by randomness and ambiguity of the entropy, and reflects the cohesiveness of uncertainty of all points representing a specific language value in a domain space, and the size of the cohesiveness can represent the dispersion and thickness of cloud;
k is a constant set according to the condition of the comment, and the fuzzy degree of the comment is embodied; [ C ]min，Cmax]Representing the WPI value range corresponding to each evaluation grade in the comment set;
when only one-sided constraint comment exists, the cloud parameters are determined by combining the upper limit and the lower limit of the data, the parameters of the default boundary are determined, and then calculation is carried out according to the formula; on the basis of the formula, the obtained cloud parameter determination method is as follows:
1) cloud parameters corresponding to evaluation interval 1(0, a):
Ex1＝0
En1＝a/3
He1＝k
2) evaluation section i (C)min，Cmax) (0 < i < n) corresponding cloud parameters:
Exi＝(Cmin+Cmax)/2
Eni＝(Cmax-Cmin)/6
Hei＝k
3) cloud parameters corresponding to the evaluation interval n (m, + ∞):
Ex＝Cmin′+Cmax′
En＝Cmin′/3
He＝k
Cmin′、Cmax' upper and lower limits of the evaluation interval n-1, respectively;
step b, generating a cloud model;
evaluating cloud model parameters Ex, En and He according to the determined WPI value, and generating a corresponding WPI value evaluation cloud chart by using a normal cloud generator;
the specific operation process is as follows:
inputting: cloud model digital features (Ex, En, He) and the number n of generated cloud droplets;
and (3) outputting: quantitative data x of n cloud dropletsiAnd degree of certainty y of its corresponding qualitative concepti(i＝1,2,…,n)；
The specific algorithm steps are as follows:
1: generating the desired value, He, of En2A normal random number Enn that is the variance;
2: generation of expected value of Ex, Enn2A normal random number x being the varianceiI.e. xiThe method is a specific quantitative realization of a qualitative concept A on a corresponding quantitative discourse domain, and is called cloud drop quantitative data;
3: computing
Definition of yiIs xiDegree of certainty belonging to qualitative concept A, (x)i,yi) Is cloud drop;
4: repeating the above steps until n cloud droplets (x) are generatedi,yi) (i ═ n) up to.
3. The comprehensive water quality evaluation method based on the water pollution index method and the cloud model according to claim 1, wherein the step 3 is performed according to the following steps:
step a, calculating WPI values corresponding to water quality evaluation factors based on a water pollution index method;
the water pollution index method is based on the evaluation principle of a single-factor evaluation method, and the WPI value of each participating water quality evaluation item of a certain section is calculated by an interpolation method according to the water quality category and the WPI value corresponding table, and the highest WPI value is taken as the WPI value of the section;
the WPI value calculation formula of each water quality evaluation factor is as follows:
1) the WPI value calculation method for the index when the water pollution index does not exceed the class V water limit value is as follows:
Cl(i)＜C(i)≤Ch(i)
wherein C (i) is the actual monitoring value of the ith water quality index, Cl(i)、Ch(i) Respectively the lower limit value and the upper limit value of the classification standard of the ith water quality index, WPIl(i)、WPIh(i) Respectively under the category standard of the ith water quality indexThe WPI (i) is an index value corresponding to the ith water quality index;
furthermore, when 6< pH <9,
WPI(pH)＝20；
WIP (pH) is the WPI value corresponding to the index pH value;
2) and when the water limit value of the class V is exceeded, the WPI value calculation method comprises the following steps:
in the formula, C5(i) Is the standard concentration limit value of the class V in the water quality category of the ith project;
furthermore, when the pH is <6,
WPI(pH)＝100+6.67×(6-pH)
when the pH is greater than 9, the pH is adjusted,
WPI(pH)＝100+8.00×(pH-9)；
step b, generating a single-factor WPI value cloud model;
computing parameters of a cloud model using an inverse cloud algorithm based on X information, using cloud droplet X (X) onlyi) To reduce the three parameters of the cloud without the need for a degree of certainty Y (Y)i) The operation process is as follows:
inputting: sample point xi(i＝1,2,…,n)；
And (3) outputting: the n cloud droplets correspond to the cloud model numerical features (Ex, En, He) of the qualitative concept;
the algorithm comprises the following steps:
1: according to xiCalculating the mean of the samples for the set of dataAbsolute center distance of first order sampleSample variance
2: calculating an expectation
3: computing entropy
4: computing hyper-entropy
Cloud drop xiThe WPI value is the WPI value corresponding to the water quality evaluation index actual monitoring data collected in the actual application.
4. The comprehensive water quality evaluation method based on the water pollution index method and the cloud model according to claim 1, wherein the step 4 is performed according to the following steps: and (3) synthesizing the single-factor WPI value cloud model by applying a comprehensive cloud algorithm and combining the weight to generate a comprehensive WPI value cloud model, wherein the formula of the comprehensive cloud model is as follows:
5. the comprehensive water quality evaluation method based on the water pollution index method and the cloud model according to claim 1, wherein the step 5 is performed according to the following steps: and generating a WPI value evaluation cloud picture and a comprehensive WPI value cloud picture by using a normal cloud generator, comparing, and finally determining the comprehensive water quality evaluation grade.
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