CN111832927A - Method for evaluating potential risk of environmental quality of coastal wetland ecosystem - Google Patents

Method for evaluating potential risk of environmental quality of coastal wetland ecosystem Download PDF

Info

Publication number
CN111832927A
CN111832927A CN202010637865.4A CN202010637865A CN111832927A CN 111832927 A CN111832927 A CN 111832927A CN 202010637865 A CN202010637865 A CN 202010637865A CN 111832927 A CN111832927 A CN 111832927A
Authority
CN
China
Prior art keywords
index
evaluation index
content
matrix
level
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010637865.4A
Other languages
Chinese (zh)
Inventor
崔正国
江涛
白莹
胡清静
崔鸿武
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
Original Assignee
Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences filed Critical Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
Priority to CN202010637865.4A priority Critical patent/CN111832927A/en
Publication of CN111832927A publication Critical patent/CN111832927A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Mathematical Optimization (AREA)
  • Software Systems (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Computational Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computing Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Algebra (AREA)
  • Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Quality & Reliability (AREA)
  • Databases & Information Systems (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Biomedical Technology (AREA)
  • Fuzzy Systems (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The embodiment of the invention discloses a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem, which comprises the following steps: step S1: establishing an index system, wherein the index system comprises evaluation indexes of a three-level hierarchical structure; step S2: determining each level of evaluation index weight matrix of the index system by adopting an analytic hierarchy process; step S3: determining a fuzzy discrimination matrix of the third-level evaluation index by adopting a fuzzy comprehensive judgment method; step S4: and calculating the potential risk index in a grading manner and evaluating the potential risk grade according to the weight matrix of the evaluation index at each grade and the fuzzy discrimination matrix of the evaluation index at the third grade. The embodiment of the invention improves the evaluation efficiency of the potential risks of the ecological environment quality of the coastal wetland and has high practicability.

Description

Method for evaluating potential risk of environmental quality of coastal wetland ecosystem
Technical Field
The invention belongs to the technical field of wetland ecological environment protection, and particularly relates to a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem.
Background
The coastal wetland is a land source polluted drainage area and transfer area, and pollutants input into the environment change the physical and chemical characteristics of the environment and the material basis of aquatic organisms through various physical, chemical and biological migration and transformation processes, so that the structure and the function of the whole aquatic organism wetland system are damaged, and the safety and the health of the whole ecological system are endangered. The potential ecological risks of the environmental quality of the coastal wetland are objectively evaluated, and a corresponding standard control system is established, so that the method has important significance for preventing and controlling the ecological risks of the coastal wetland system.
Current methods of assessing potential ecological risks fall broadly into two categories. The method is a single-factor evaluation method, and single-factor evaluation of potential ecological risks is carried out by using a quality fraction index mode. The second is a comprehensive index method, which is a cumulative earth index (Igeo) method and a potential ecological hazard index (RI) method. In recent years, mathematical theories such as an analytic hierarchy process, an index evaluation method, a fuzzy evaluation method, an artificial neural network evaluation method, a quality grading method, a principal component analysis method and the like are gradually applied to potential ecological risk assessment, and researches are trying to deeply understand and understand various aspects such as the structure, the function, the process, the dynamic state and the like of the wetland from different angles. However, in general, the contents related to wetland functionality, ecological characteristics and social benefits are more, the research on wetland ecological environment quality monitoring and evaluation is less, a set of coastal wetland ecological environment quality potential risk evaluation system suitable for China is not established, and a targeted coastal wetland ecological environment quality potential risk evaluation method is also lacked.
In conclusion, in order to solve the problem of lack of the coastal wetland ecological environment quality potential risk assessment system and method, it is particularly necessary to design a coastal wetland ecological environment quality potential risk assessment method.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem. The method comprehensively considers the potential risks of the environmental quality of the coastal wetland reflected by the physical, chemical and biological migration and transformation processes of the substances, builds a set of multi-level structure evaluation system based on three different environments of water environment, deposition environment and organism quality, integrates a fuzzy evaluation method and an analytic hierarchy process for evaluation, ensures the scientificity and rationality of evaluation results, and can provide data support for preventing and controlling the ecological risks of the coastal wetland system.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem comprises the following steps:
step S1: establishing an index system, wherein the index system comprises evaluation indexes of a three-level hierarchical structure;
step S2: determining each level of evaluation index weight matrix of the index system by adopting an analytic hierarchy process;
step S3: determining a fuzzy discrimination matrix of the third-level evaluation index by adopting a fuzzy comprehensive judgment method;
step S4: and calculating the potential risk index in a grading manner and evaluating the potential risk grade according to the weight matrix of the evaluation index at each grade and the fuzzy discrimination matrix of the evaluation index at the third grade.
Further, in step S1, the first-level evaluation index includes: water ecological environment monitoring index u1And monitoring index u of deposition ecological environment2Biological quality monitoring index u3。
Further, the water ecological environment monitoring index u1The second-stage evaluation index of (1) includes: organic factor u11Microbial factor u12Heavy metal factor u13Beethrin u14；
The sedimentary ecological environment monitoring index u2The second-stage evaluation index of (1) includes: organic factor u21Microbial factor u22Heavy metal factor u23Beethrin u24；
The organism quality monitoring indexu3The second-stage evaluation index of (1) includes: organic factor u31Microbial factor u32Heavy metal factor u33Beethrin u34。
Further, the first-level assessment index is a water ecological environment monitoring index u1Second-stage evaluation index organic factor u11The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u111And a content u of benzene hexachloride112Bis-p-chlorophenyl trichloroethane content u113Poly (chlorobiphenyl) content u114；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index microbial factor u12The third-level evaluation index of (1) includes: number of bacteria u121The number u of Escherichia coli122Fecal coliform number u123Coliform group number u124Total number of bacteria u125；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index heavy metal factor u13The third-level evaluation index of (1) includes: copper content u131Zinc content u132Lead content u133Cadmium content u134Mercury content u135Arsenic content u136Chromium content u137；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index beta toxin factor u14The third-level evaluation index of (1) includes: paralytic shellfish poison content u141Content u of diarrhea-causing shellfish poison142；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index organic factor u21The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u211And a content u of benzene hexachloride212Bis-p-chlorophenyl trichloroethane content u213Poly (chlorobiphenyl) content u214；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index microbial factor u22Third grade of (3)The estimation indexes include: number of bacteria u221The number u of Escherichia coli222Fecal coliform number u223Coliform group number u224Total number of bacteria u225；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index heavy metal factor u23The third-level evaluation index of (1) includes: copper content u231Zinc content u232Lead content u233Cadmium content u234Mercury content u235Arsenic content u236Chromium content u237；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index beta toxin factor u24The third-level evaluation index of (1) includes: paralytic shellfish poison content u241Content u of diarrhea-causing shellfish poison242；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index organic factor u31The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u311And a content u of benzene hexachloride312Bis-p-chlorophenyl trichloroethane content u313Poly (chlorobiphenyl) content u314；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index microbial factor u32The third-level evaluation index of (1) includes: number of bacteria u321The number u of Escherichia coli322Fecal coliform number u323Coliform group number u324Total number of bacteria u325；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index heavy metal factor u33The third-level evaluation index of (1) includes: copper content u331Zinc content u332Lead content u333Cadmium content u334Mercury content u335Arsenic content u336Chromium content u337；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index beta toxin factor u34The third-level evaluation index of (1) includes: paralytic shellfish poison content u341Content u of diarrhea-causing shellfish poison342。
Further, the step S2 includes the following steps:
step S2.1: constructing a third-level evaluation index u by adopting a 1-5 scale methodkliRegarding the second-stage evaluation index uklImportance determination matrix HklSecond-level evaluation index uklWith respect to the first-order evaluation index ukImportance determination matrix HkFirst-order evaluation index ukA judgment matrix H about the evaluation importance of the potential risks of the ecological environment quality of the coastal wetland; wherein K is 1,2, … …, K representing the number of first-stage evaluation indexes; l is 1,2, … …, L denotes the first-stage evaluation index ukThe number of included second-level evaluation indicators; i is 1,2, … …, I denotes the second-stage evaluation index uklThe number of third-level evaluation indicators included;
step S2.2: for each decision matrix Hkl、HkH, respectively calculating the maximum eigenvalue lambdamaxAnd maximum eigenvalue λmaxCorresponding to the feature vector W;
step S2.3: for each decision matrix Hkl、HkH, identity check according to their identity ratio CR, respectively: if CR is less than or equal to 0.1, the consistency of each target of the judgment matrix H is good, and W is the required characteristic vector; otherwise, readjusting the judgment matrix, and repeating the step S2.2 and the step S2.3;
step S2.4: for each decision matrix Hkl、HkH, respectively normalizing the characteristic vectors W to obtain a weight matrix
Figure BDA0002570156510000041
W*。
Further, in step S2.3, the mathematical expression of the consistency ratio CR is
Figure BDA0002570156510000042
Wherein
Figure BDA0002570156510000043
n is the order number of the corresponding judgment matrix, and RI is the random consistency index of the n-order matrix.
Further, the step S3 includes the following steps:
step S3.1: all the third-stage evaluation indexes are composed of a factor set U, and are expressed as U ═ UkliWhere k is 1,2,3, l is 1,2, …,4, i is 1,2, …, 7;
step S3.2: determining evaluation criterion submatrices S corresponding to the comment set V and the comment set V, wherein V is ═ Vj|j＝1,2,…,n}、S＝(sj)1×jN is the number of elements of the comment set V;
step S3.3: each third-level evaluation index U for the factor set UkliDetermining ukliElements V relating to the comment set VjMembership function u ofklivj(xi) Constitute a third-level evaluation index ukliFuzzy discrimination matrix r ofkli＝[ukliv1(x),ukliv2(x),…,uklivn(x)]。
Further, in step S3.2, the comment set V includes five elements of minimal risk, mild risk, severe risk, high risk and very high risk, and is expressed as comment set V ═ { V ═ V1,v2,v3,v4,v5}；
Each element V of the comment set V1，v2，v3，v4，v5One-to-one correspondence evaluation criterion score of 0, 25, 50, 75, 100 is expressed as the evaluation criterion scoring matrix S ═ 0255075100]。
Further, in step S3.3, the third-level evaluation index ukliWith respect to elements V of comment set V1，v2，v3，v4，v5Membership function u ofkliv1(x)、ukliv2(x)、ukliv3(x)、ukliv4(x)、ukliv5(x) The mathematical expressions of (a) are respectively:
Figure BDA0002570156510000051
Figure BDA0002570156510000052
Figure BDA0002570156510000053
wherein C iskli1、Ckli2、Ckli3、Ckli4、Ckli5Respectively represents ukliAbout v1，v2，v3，v4，v5Has an evaluation criterion threshold value of Ckli1＜Ckli2＜Ckli3＜Ckli4＜Ckli5。
Further, the step S4 includes the following steps:
step S4.1: according to the index weight matrix of each layer
Figure BDA0002570156510000054
W*And fuzzy discrimination matrix r of third-level evaluation indexkliSuccessively calculating fuzzy discrimination matrix r of second-level evaluation indexklFuzzy discrimination matrix r of first-level evaluation indexkA fuzzy discrimination matrix r of potential risks of the ecological environment quality of the coastal wetland;
step S4.2: fuzzy discrimination matrix r according to evaluation indexes of all levelskl、rkR and an evaluation criterion submatrix S are used for respectively calculating potential risk indexes p ═ r at each stageklST、p＝r1ST、p＝rSTIn which S isTTransposing for S;
step S4.3: respectively determining the potential risk grades of all levels of evaluation indexes according to the potential risk indexes p: when p is more than or equal to 0 and less than or equal to 12.5, the potential risk is first grade; when p is more than 12.5 and less than or equal to 37.5, the potential risk is second grade; when p is more than 37.5 and less than or equal to 62.5, the potential risk is three levels; when p is more than 62.5 and less than or equal to 87.5, the potential risk is four levels; if p is more than 87.5 and less than or equal to 100, the potential risk is five grades.
Further, the step S4.1 includes the steps of:
step S4.1.1: evaluating the index u for each second levelklAccording to u, respectivelyklThird-level evaluation index ukliIs constructed by the fuzzy discriminant matrix rkliConstructing a single-factor evaluation matrix R thereofkl，
Figure BDA0002570156510000061
And combining the third-level evaluation index ukliFor the second level evaluation index uklWeight vector of importance
Figure BDA0002570156510000062
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000063
Step S4.1.2: evaluating the index u for each first levelkAccording to u, respectivelykSecond-stage evaluation index uklFuzzy discrimination matrix r ofkiConstructing a single-factor evaluation matrix R thereofk，
Figure BDA0002570156510000064
And combining the second evaluation index uklFor the first-order evaluation index ukWeight vector of importance
Figure BDA0002570156510000065
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000066
Step S4.1.3: according to the first-level evaluation index ukFuzzy discrimination matrix r ofkConstructing single-factor evaluation moment for evaluating potential risks of coastal wetland ecological environment qualityThe matrix R is a matrix of a plurality of arrays,
Figure BDA0002570156510000067
and combining the first evaluation index ukWeight vector W for evaluating importance of potential risks of coastal wetland ecological environment quality*Calculating a fuzzy discrimination matrix r ═ W for evaluating potential risks of the ecological environment quality of the coastal wetland*·R。
The invention has the beneficial effects that: (1) the method comprehensively considers the potential risks of the environmental quality of the coastal wetland reflected by the physical, chemical and biological migration and transformation processes of the substances, and builds a set of comprehensive and complete evaluation system by monitoring indexes of various factors such as organic matters, heavy metals, microorganisms and the like under three different environments of water environment, deposition environment and organism quality, wherein the monitoring indexes can be quantized and are convenient for relevant personnel to obtain, and the method has strong operability; (2) on the basis of referring to expert opinions, the method adopts an analytic hierarchy process to calculate the index weights of all levels, adopts a fuzzy comprehensive evaluation method to evaluate the potential risks of all levels, and enables the calculation result to be more scientific and reasonable; (3) the method can realize multi-level evaluation of potential risks of the coastal ecological environment quality, and can provide richer data support for preventing and controlling the ecological risks of the coastal wetland system.
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, and 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 these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below 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.
Referring to fig. 1, the embodiment discloses a method for evaluating potential risks of environmental quality of a coastal wetland ecosystem, which comprises the following steps:
s1: establishing an index system, wherein the index system comprises evaluation indexes of a three-level hierarchical structure;
s2: determining evaluation index weight matrixes of all levels of an index system by adopting an analytic hierarchy process;
s3: determining a fuzzy discrimination matrix of the third-level evaluation index by adopting a fuzzy comprehensive judgment method;
s4: and calculating the potential risk index in a grading manner and evaluating the potential risk grade according to the weight matrix of the evaluation index at each grade and the fuzzy discrimination matrix of the evaluation index at the third grade.
Further, the first-level evaluation index of the coastal wetland ecological environment quality potential risk evaluation index system in the step S1 includes: water ecological environment monitoring index u1And monitoring index u of deposition ecological environment2Biological quality monitoring index u3。
Further, the first-stage assessment index is a water ecological environment monitoring index u1The second-stage evaluation index of (1) includes: organic factor u11Microbial factor u12Heavy metal factor u13Beethrin u14；
First-level index deposition ecological environment monitoring index u2The second-stage evaluation index of (1) includes: organic factor u21Microbial factor u22Heavy metal factor u23Beethrin u24；
First-order index biological quality monitoring index u3The second-stage evaluation index of (1) includes: organic factor u31Microbial factor u32Heavy metal factor u33Beethrin u34；
Further, the first-stage assessment index is a water ecological environment monitoring index u1Second-stage evaluation index organic factor u11The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u111And a content u of benzene hexachloride112Bis-p-chlorophenyl trichloroethane content u113Poly (chlorobiphenyl) content u114；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index microbial factor u12The third-level evaluation index of (1) includes: number of bacteria u121The number u of Escherichia coli122Fecal coliform number u123Coliform group number u124Total number of bacteria u125；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index heavy metal factor u13The third-level evaluation index of (1) includes: copper content u131Zinc content u132Lead content u133Cadmium content u134Mercury content u135Arsenic content u136Chromium content u137；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index beta toxin factor u14The third-level evaluation index of (1) includes: paralytic shellfish poison content u141Content u of diarrhea-causing shellfish poison142；
First-stage assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index organic factor u21The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u211And a content u of benzene hexachloride212Bis-p-chlorophenyl trichloroethane content u213Poly (chlorobiphenyl) content u214；
First-stage assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index microbial factor u22The third-level evaluation index of (1) includes: number of bacteria u221The number u of Escherichia coli222Fecal coliform number u223Coliform group number u224Total number of bacteria u225；
First-stage assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index heavy metal factor u23The third-level evaluation index of (1) includes: copper content u231Zinc content u232Lead content u233Cadmium content u234Mercury content u235Arsenic content u236Chromium content u237；
First-stage assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index beta toxin factor u24The third-level evaluation index of (1) includes: paralytic shellfish poison content u241Content u of diarrhea-causing shellfish poison242；
First-order assessment index organism quality monitoring index u3Second-stage evaluation index organic factor u31The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u311And a content u of benzene hexachloride312Bis-p-chlorophenyl trichloroethane content u313Poly (chlorobiphenyl) content u314；
First-order assessment index organism quality monitoring index u3Second-stage evaluation index microbial factor u32The third-level evaluation index of (1) includes: number of bacteria u321The number u of Escherichia coli322Fecal coliform number u323Coliform group number u324Total number of bacteria u325；
First-order assessment index organism quality monitoring index u3Second-stage evaluation index heavy metal factor u33The third-level evaluation index of (1) includes: copper content u331Zinc content u332Lead content u333Cadmium content u334Mercury content u335Arsenic content u336Chromium content u337；
First-order assessment index organism quality monitoring index u3Second-stage evaluation index beta toxin factor u34The third-level evaluation index of (1) includes: paralytic shellfish poison content u341Content u of diarrhea-causing shellfish poison342；
Further, step S2 includes the following steps:
s2.1: constructing a third-level evaluation index u by adopting a 1-5 scale methodkliRegarding the second-stage evaluation index uklImportance determination matrix HklSecond-level evaluation index uklWith respect to the first-order evaluation index ukImportance determination HkFirst-order evaluation index ukA judgment matrix H about the importance of evaluation of potential risks of the ecological environment quality of the coastal wetland, wherein K is 1,2, … … and K, and K represents the number of first-level evaluation indexes; l is 1,2, … …, L denotes the first-stage evaluation index ukThe number of included second-level evaluation indicators; i is 1,2, … …, I denotes the second-stage evaluation index uklThe number of third-level evaluation indicators included;
s2.2: for each decision matrix Hkl、HkH, respectively calculating the maximum eigenvalue lambdamaxAnd maximum eigenvalue λmaxCorresponding to the feature vector W;
s2.3: for each decision matrix Hkl、HkH, identity check according to their identity ratio CR, respectively: if CR is less than or equal to 0.1, the consistency of each target of the judgment matrix H is good, and W is the required characteristic vector; otherwise, readjusting the judgment matrix, and repeating S2.2 and S2.3;
s2.4: for each decision matrix Hkl、HkH, respectively normalizing the characteristic vectors W to obtain a weight matrix
Figure BDA0002570156510000091
W*。
Further, the mathematical expression of the consistency ratio CR in step S2.3 is
Figure BDA0002570156510000092
Wherein
Figure BDA0002570156510000093
n is the order number of the corresponding judgment matrix, and RI is the random consistency index of the n-order matrix.
Further, step S3 includes the following steps:
s3.1: all the third-stage evaluation indexes are composed of a factor set U, and are expressed as U ═ UkliWhere k is 1,2,3, l is 1,2, …,4, i is 1,2, …, 7;
s3.2: determining evaluation criterion submatrices S corresponding to the comment set V and the comment set V, wherein V is ═ Vj|j＝1,2,…,n}、S＝(sj)1×jN is the number of elements of the comment set V;
s3.3: each third-level evaluation index U for a factor set UkliDetermining ukliElements V relating to comment set VjMembership function u ofklivj(xi) Constitute a third-level evaluation index ukliFuzzy discrimination matrix r ofkli＝[ukliv1(x),ukliv2(x),…,uklivn(x)]。
Further, the panel of comments V in the definition step S3.2 includes five elements of minimal risk, mild risk, severe risk, high risk, and very high risk, which is expressed as panel of comments V ═ { V ═ V1,v2,v3,v4,v5}；
Definition comment set V each element V1，v2，v3，v4，v5The evaluation criteria are assigned 0, 25, 50, 75, 100 in a one-to-one correspondence, and expressed as an evaluation criteria assignment matrix S ═ 0255075100]。
Further, a three-level evaluation index u is definedkliWith respect to elements V of comment set V1，v2，v3，v4，v5Membership function u ofkliv1(x)、ukliv2(x)、ukliv3(x)、ukliv4(x)、ukliv5(x) The mathematical expressions of (a) are respectively:
Figure BDA0002570156510000101
Figure BDA0002570156510000102
Figure BDA0002570156510000103
wherein C iskli1、Ckli2、Ckli3、Ckli4、Ckli5Respectively represents ukliAbout v1，v2，v3，v4，v5Has an evaluation criterion threshold value of Ckli1＜Ckli2＜Ckli3＜Ckli4＜Ckli5。
For Ckli1、Ckli2、Ckli3、Ckli4、Ckli5The determination is made based on three reference values: (1) taking the evaluation standard which is accepted by national standard, industry standard, local standard or industry as a reference value; (2) adopting a background value method, and taking survey and monitoring data before 1985 as a reference value; (3) and adopting a contrast zone method, and adopting monitoring data of the coastal wetland similar to the type of an evaluation object, the climate condition, the coastal social development condition and the like as a reference value.
Further, step S4 includes the following steps:
s4.1: according to each level index weight
Figure BDA0002570156510000111
W*And fuzzy discrimination matrix r of third-level evaluation indexkliSuccessively calculating fuzzy discrimination matrix r of second-level evaluation indexklFuzzy discrimination matrix r of first-level evaluation indexkA fuzzy discrimination matrix r of potential risks of the ecological environment quality of the coastal wetland;
s4.2: fuzzy discrimination matrix r according to evaluation indexes of all levelskl、rkR and an evaluation criterion submatrix S are used for respectively calculating potential risk indexes p ═ r at each stageklST、p＝r1ST、p＝rSTIn which S isTTranspose for S.
S4.3: respectively determining potential risk levels of all levels of evaluation indexes according to the potential risk indexes p: when p is more than or equal to 0 and less than or equal to 12.5, the potential risk is first grade; when p is more than 12.5 and less than or equal to 37.5, the potential risk is second grade; when p is more than 37.5 and less than or equal to 62.5, the potential risk is three levels; when p is more than 62.5 and less than or equal to 87.5, the potential risk is four levels; if p is more than 87.5 and less than or equal to 100, the potential risk is five grades.
Further, step S4.1 comprises the steps of:
s4.1.1: evaluating the index u for each second levelklAccording to u, respectivelyklThird-level evaluation index ukliIs constructed by the fuzzy discriminant matrix rkliConstructing a single-factor evaluation matrix thereof
Figure BDA0002570156510000112
And further combining with a third-level evaluation index ukliFor the second level evaluation index uklWeight vector of importance
Figure BDA0002570156510000113
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000114
S4.1.2: evaluating the index u for each first levelkAccording to u, respectivelykSecond-stage evaluation index uklFuzzy discrimination matrix r ofkiConstructing a single-factor evaluation matrix thereof
Figure BDA0002570156510000115
And further combining a second-level evaluation index uklFor the first-order evaluation index ukWeight vector of importance
Figure BDA0002570156510000116
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000117
S4.1.3: according to the first-level evaluation index ukFuzzy discrimination matrix r ofkConstructing a single-factor evaluation matrix for evaluating potential risks of coastal wetland ecological environment quality
Figure BDA0002570156510000118
And further combining the first-level evaluation index ukWeight vector W for evaluating importance of potential risks of coastal wetland ecological environment quality*Calculating a fuzzy discrimination matrix r ═ W for evaluating potential risks of the ecological environment quality of the coastal wetland*·R。
The method comprehensively considers the potential risks of the environmental quality of the coastal wetland reflected by the physical, chemical and biological migration and transformation processes of the substances, builds a set of multi-level structure evaluation system based on three different environments of water environment, deposition environment and organism quality, integrates a fuzzy evaluation method and an analytic hierarchy process for evaluation, ensures the scientificity and rationality of evaluation results, and can provide data support for preventing and controlling the ecological risks of the coastal wetland system.
In order to describe the embodiment of the method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem according to the present invention in detail, the method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem according to the present invention will be described in detail with specific examples for easy understanding.
S1: establishing a coastal wetland ecological environment quality potential risk assessment index system comprising a three-level hierarchical structure;
taking a piece of monitoring data of a certain coastal wetland in northeast China in 2018 months as an example, a coastal ecological environment quality potential risk assessment index system and actual measurement data thereof are shown in the following table 1:
TABLE 1 sea ecology environment quality potential risk assessment index system and measured data example
Figure BDA0002570156510000121
Figure BDA0002570156510000131
Figure BDA0002570156510000141
S2: determining evaluation index weight matrixes of all levels of an index system by adopting an analytic hierarchy process, wherein the evaluation index weight matrixes comprise:
s2.1: constructing a third-level evaluation index u by adopting a 1-5 scale methodkliRegarding the second-stage evaluation index uklImportance determination matrix HklSecond-level evaluation index uklWith respect to the first-order evaluation index ukImportance determination HkFirst-order evaluation index ukA judgment matrix H about the importance of evaluation of potential risks of the ecological environment quality of the coastal wetland, wherein K is 1,2, … … and K, and K represents the number of first-level evaluation indexes; l is 1,2, … …, L denotes the first-stage evaluation index ukThe number of included second-level evaluation indicators; i is 1,2, … …, I denotes the second-stage evaluation index uklThe number of third-level evaluation indicators included;
in this example, the third-level evaluation index is the number u of bacteria121The number u of Escherichia coli122Fecal coliform group u123Coliform group u124Total number of bacteria u125Second-stage evaluation index microbial factor u in water ecological environment12Is determined by
Figure BDA0002570156510000142
S2.2: for each decision matrix Hkl、HkH, respectively calculating the maximum eigenvalue lambdamaxAnd maximum eigenvalue λmaxCorresponding to the feature vector W;
such as calculating H12Obtaining the maximum eigenvalue lambdamaxIs 5.01, maximum eigenvalue lambdamaxThe corresponding characteristic vector W is [ -0.723-0.382-0.382-0.382-0.203 [ -0.723-0.382 [ ]]。
S2.3: for each decision matrix Hkl、HkH, identity check according to their identity ratio CR, respectively: if CR is less than or equal to 0.1, the consistency of each target of the judgment matrix H is good, and W is the required characteristic vector; otherwise, readjusting the judgment matrix, and repeating S2.2 and S2.3;
such as calculating H12Has a consistency ratio of
Figure BDA0002570156510000151
Description of the invention H12The requirements are met, and readjustment is not needed;
s2.4: for each decision matrix Hkl、HkH, respectively normalizing W to the characteristic vectors W to obtain a weight matrix
Figure BDA0002570156510000152
W*。
Such as H12Weight matrix obtained by normalizing feature vector W
Figure BDA0002570156510000153
The final evaluation index judgment matrix and the final weight matrix at each level are shown in the following table 2:
TABLE 2 evaluation index judgment matrix and weight matrix at each level
Figure BDA0002570156510000154
Figure BDA0002570156510000161
Figure BDA0002570156510000171
S3: determining a fuzzy discrimination matrix of a third-level evaluation index by adopting a fuzzy comprehensive judgment method, wherein the fuzzy discrimination matrix comprises the following steps:
s3.1: all the third-stage evaluation indexes are composed of a factor set U, and are expressed as U ═ UkliWhere k is 1,2,3, l is 1,2, …,4, i is 1,2, …, 7;
s3.2: determining evaluation criterion submatrices S corresponding to the comment set V and the comment set V, wherein V is ═ Vj|j＝1,2,…,n}、S＝(sj)1×jN is the number of elements of the comment set V;
s3.3: each third-level evaluation index U for a factor set UkliDetermining ukliElements V relating to comment set VjMembership function u ofklivj(xi) Constitute a third-level evaluation index ukliFuzzy discrimination matrix r ofkli＝[ukliv1(x),ukliv2(x),…,uklivn(x)]。
Definition in step S3.2 the comment set V includes five elements of minimal risk, mild risk, severe risk, high risk, and very high risk, denoted as comment set V ═ { V ═ V1,v2,v3,v4,v5}；
Definition comment set V each element V1，v2，v3，v4，v5The evaluation criteria are assigned 0, 25, 50, 75, 100 in a one-to-one correspondence, and expressed as an evaluation criteria assignment matrix S ═ 0255075100]。
Defining a third-level evaluation index ukliWith respect to elements V of comment set V1，v2，v3，v4，v5Membership function u ofkliv1(x)、ukliv2(x)、ukliv3(x)、ukliv4(x)、ukliv5(x) The mathematical expressions of (a) are respectively:
Figure BDA0002570156510000181
Figure BDA0002570156510000182
Figure BDA0002570156510000183
wherein C iskli1、Ckli2、Ckli3、Ckli4、Ckli5Respectively represents ukliAbout v1，v2，v3，v4，v5Is a threshold value of the evaluation criterion of (1), andCkli1＜Ckli2＜Ckli3＜Ckli4＜Ckli5。
each third-level evaluation index v of the embodiment1，v2，v3，v4，v5Is evaluated with respect to the standard threshold value, and v is calculated1，v2，v3，v4，v5The result of the calculation of the fuzzy discrimination matrix applied to each third-stage evaluation index is shown in table 3 below:
TABLE 3 evaluation standard threshold and fuzzy discriminant matrix result of each third-level index
Figure BDA0002570156510000184
Figure BDA0002570156510000191
Figure BDA0002570156510000201
S4: calculating the potential risk index in a grading way and evaluating the potential risk grade according to the index weight of each layer and the fuzzy discrimination matrix of the third-level evaluation index, wherein the evaluation comprises the following steps:
s4.1: according to each level index weight
Figure BDA0002570156510000202
W*And fuzzy discrimination matrix r of third-level evaluation indexkliSuccessively calculating fuzzy discrimination matrix r of second-level evaluation indexklFuzzy discrimination matrix r of first-level evaluation indexkAnd a fuzzy discrimination matrix r of potential risks of the ecological environment quality of the coastal wetland. The process comprises the following substeps:
s4.1.1: evaluating the index u for each second levelklAccording to u, respectivelyklThird-level evaluation index ukliFuzzy discrimination matrix r ofkliConstructing a single-factor evaluation matrix thereof
Figure BDA0002570156510000211
And further combining with a third-level evaluation index ukliFor the second level evaluation index uklWeight vector of importance
Figure BDA0002570156510000212
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000213
By using the weight matrix of the third-stage evaluation index in table 2 with respect to the respective corresponding second-stage evaluation index and the fuzzy discrimination matrix of the third-stage evaluation index in table 3, the single-factor evaluation matrix and the fuzzy discrimination matrix of the second-stage evaluation index shown in table 4 below can be obtained:
TABLE 4 Single-factor evaluation matrix and fuzzy discriminant matrix of second-level evaluation index
Figure BDA0002570156510000214
Figure BDA0002570156510000221
S4.1.2: evaluating the index u for each first levelkAccording to u, respectivelykSecond-stage evaluation index uklFuzzy discrimination matrix r ofkiConstructing a single-factor evaluation matrix thereof
Figure BDA0002570156510000222
And further combining a second-level evaluation index uklFor the first-order evaluation index ukWeight vector of importance
Figure BDA0002570156510000223
Calculating its fuzzy discriminant matrix
Figure BDA0002570156510000224
By using the weight matrix of the second-stage evaluation index in table 2 with respect to the respective corresponding first-stage evaluation index and the fuzzy discrimination matrix of the second-stage evaluation index in table 4, the single-factor evaluation matrix and the fuzzy discrimination matrix of the first-stage evaluation index shown in table 5 below can be obtained:
TABLE 5 Single-factor evaluation matrix and fuzzy discriminant matrix for first-level evaluation index
Figure BDA0002570156510000225
Figure BDA0002570156510000231
S4.1.3: evaluating a first-level evaluation index u according to the potential risk of the ecological environment quality of the coastal wetlandkFuzzy discrimination matrix r ofkConstructing a single-factor evaluation matrix for evaluating potential risks of coastal wetland ecological environment quality
Figure BDA0002570156510000232
And further combining the first-level evaluation index ukWeight vector W for evaluating importance of potential risks of coastal wetland ecological environment quality*Calculating a fuzzy discrimination matrix r ═ W for evaluating potential risks of the ecological environment quality of the coastal wetland*·R。
By using the weight matrix of the first-level evaluation index in the table 2 for evaluating the potential risk of the ecological environment quality of the coastal wetland and the fuzzy discrimination matrix of the first-level evaluation index in the table 4, the evaluation method can obtain the evaluation result
Figure BDA0002570156510000233
r＝[0.031 0.086 0.187 0.311 0.383]。
S4.2: fuzzy discrimination matrix r according to evaluation indexes of all levelskl、rkR and an evaluation criterion submatrix S are used for respectively calculating potential risk indexes p ═ r at each stageklST、p＝r1ST、p＝rSTIn which S isTTranspose for S.
S4.3: respectively determining potential risk levels of all levels of evaluation indexes according to the potential risk indexes p: when p is more than or equal to 0 and less than or equal to 12.5, the potential risk is first grade; when p is more than 12.5 and less than or equal to 37.5, the potential risk is second grade; when p is more than 37.5 and less than or equal to 62.5, the potential risk is three levels; when p is more than 62.5 and less than or equal to 87.5, the potential risk is four levels; if p is more than 87.5 and less than or equal to 100, the potential risk is five grades.
Steps S4.2 and S4.3 are executed to obtain the normalized fuzzy matrix, the potential risk index and the risk level corresponding thereto as shown in table 6:
TABLE 6 potential Risk indices and Risk ratings
Index (I) Index of potential risk Potential risk rating
u11 100.0 Five stages
u12 87.8 Five stages
u13 100.0 Five stages
u14 100.0 Five stages
u21 46.6 Three-stage
u22 62.8 Four stages
u23 33.4 Second stage
u24 59.4 Three-stage
u31 73.9 Four stages
u32 72.8 Four stages
u33 70.8 Four stages
u34 71.9 Four stages
u1 96.9 Five stages
u2 50.6 Three-stage
u3 72.4 Four stages
Environmental quality risk assessment 73.1 Four stages
In an exemplary embodiment, a computer device is also provided, which comprises a memory, a processor and a program stored on the memory and executable by the processor, the processor performing the method as described above when executing the program.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as a memory comprising instructions, executable by a processor to perform the method described above is also provided. The non-transitory computer readable storage medium may be a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic tape, an optical storage device, and the like.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, it should be understood that the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
It should be understood that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. The present invention is not limited to the procedures and structures that have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (11)

1. A method for evaluating potential risks of environmental quality of a coastal wetland ecosystem is characterized by comprising the following steps:
step S1: establishing an index system, wherein the index system comprises evaluation indexes of a three-level hierarchical structure;
step S2: determining each level of evaluation index weight matrix of the index system by adopting an analytic hierarchy process;
step S3: determining a fuzzy discrimination matrix of the third-level evaluation index by adopting a fuzzy comprehensive judgment method;
step S4: and calculating the potential risk index in a grading manner and evaluating the potential risk grade according to the weight matrix of the evaluation index at each grade and the fuzzy discrimination matrix of the evaluation index at the third grade.
2. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 1, wherein in the step S1, the first-level evaluation index comprises: water ecological environment monitoring index u1And monitoring index u of deposition ecological environment2Biological quality monitoring index u3。
3. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 2, wherein the water ecological environment monitoring index u1The second-stage evaluation index of (1) includes: organic factor u11Microbial factor u12Heavy metal factor u13Beethrin u14；
The sedimentary ecological environment monitoring index u2The second-stage evaluation index of (1) includes: organic factor u21Microorganism, and microorganismPhysical factor u22Heavy metal factor u23Beethrin u24；
The organism quality monitoring index u3The second-stage evaluation index of (1) includes: organic factor u31Microbial factor u32Heavy metal factor u33Beethrin u34。
4. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem according to claim 3, wherein the first-stage evaluation index is a water ecological environment monitoring index u1Second-stage evaluation index organic factor u11The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u111And a content u of benzene hexachloride112Bis-p-chlorophenyl trichloroethane content u113Poly (chlorobiphenyl) content u114；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index microbial factor u12The third-level evaluation index of (1) includes: number of bacteria u121The number u of Escherichia coli122Fecal coliform number u123Coliform group number u124Total number of bacteria u125；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index heavy metal factor u13The third-level evaluation index of (1) includes: copper content u131Zinc content u132Lead content u133Cadmium content u134Mercury content u135Arsenic content u136Chromium content u137；
First-stage assessment index water ecological environment monitoring index u1Second-stage evaluation index beta toxin factor u14The third-level evaluation index of (1) includes: paralytic shellfish poison content u141Content u of diarrhea-causing shellfish poison142；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index organic factor u21The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u211And a content u of benzene hexachloride212Bis-p-chlorophenyl trichloroethane content u213Poly (chlorobiphenyl) content u214；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index microbial factor u22The third-level evaluation index of (1) includes: number of bacteria u221The number u of Escherichia coli222Fecal coliform number u223Coliform group number u224Total number of bacteria u225；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index heavy metal factor u23The third-level evaluation index of (1) includes: copper content u231Zinc content u232Lead content u233Cadmium content u234Mercury content u235Arsenic content u236Chromium content u237；
First-level assessment index sedimentary ecological environment monitoring index u2Second-stage evaluation index beta toxin factor u24The third-level evaluation index of (1) includes: paralytic shellfish poison content u241Content u of diarrhea-causing shellfish poison242；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index organic factor u31The third-level evaluation index of (1) includes: polycyclic aromatic hydrocarbons content u311And a content u of benzene hexachloride312Bis-p-chlorophenyl trichloroethane content u313Poly (chlorobiphenyl) content u314；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index microbial factor u32The third-level evaluation index of (1) includes: number of bacteria u321The number u of Escherichia coli322Fecal coliform number u323Coliform group number u324Total number of bacteria u325；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index heavy metal factor u33The third-level evaluation index of (1) includes: copper content u331Zinc content u332Lead content u333Cadmium content u334Mercury content u335Arsenic content u336Chromium content u337；
The first-stage evaluation index organism quality monitoring index u3Second-stage evaluation index beta toxin factor u34The third-level evaluation index of (1) includes: paralytic shellfish poison content u341Content u of diarrhea-causing shellfish poison342。
5. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem according to claim 1, wherein the step S2 comprises the following steps:
step S2.1: constructing a third-level evaluation index u by adopting a 1-5 scale methodkliRegarding the second-stage evaluation index uklImportance determination matrix HklSecond-level evaluation index uklWith respect to the first-order evaluation index ukImportance determination matrix HkFirst-order evaluation index ukA judgment matrix H about the evaluation importance of the potential risks of the ecological environment quality of the coastal wetland; wherein K is 1,2, … …, K representing the number of first-stage evaluation indexes; l is 1,2, … …, L denotes the first-stage evaluation index ukThe number of included second-level evaluation indicators; i is 1,2, … …, I denotes the second-stage evaluation index uklThe number of third-level evaluation indicators included;
step S2.2: for each decision matrix Hkl、HkH, respectively calculating the maximum eigenvalue lambdamaxAnd maximum eigenvalue λmaxCorresponding to the feature vector W;
step S2.3: for each decision matrix Hkl、HkH, identity check according to their identity ratio CR, respectively: if CR is less than or equal to 0.1, the consistency of each target of the judgment matrix H is good, and W is the required characteristic vector; otherwise, readjusting the judgment matrix, and repeating the step S2.2 and the step S2.3;
step S2.4: for each decision matrix Hkl、HkH, respectively normalizing the characteristic vectors W to obtain a weight matrix
Figure FDA0002570156500000031
W*。
6. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 5, wherein in the step S2.3, the mathematical expression of the consistency ratio CR is
Figure FDA0002570156500000032
Wherein
Figure FDA0002570156500000033
n is the order number of the corresponding judgment matrix, and RI is the random consistency index of the n-order matrix.
7. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 5, wherein the step S3 comprises the following steps:
step S3.1: all the third-stage evaluation indexes are composed of a factor set U, and are expressed as U ═ UkliWhere k is 1,2,3, l is 1,2, …,4, i is 1,2, …, 7;
step S3.2: determining evaluation criterion submatrices S corresponding to the comment set V and the comment set V, wherein V is ═ Vj|j＝1,2,…,n}、S＝(sj)1×jN is the number of elements of the comment set V;
step S3.3: each third-level evaluation index U for the factor set UkliDetermining ukliElements V relating to the comment set VjMembership function u ofklivj(xi) Constitute a third-level evaluation index ukliFuzzy discrimination matrix r ofkli＝[ukliv1(x),ukliv2(x),…,uklivn(x)]。
8. The method for evaluating potential risk of environmental quality of coastal wetland ecosystem according to claim 7Method, characterized in that in step S3.2, the comment set V includes five elements of minimal risk, mild risk, severe risk, high risk, and very high risk, denoted as comment set V ═ { V ═ V1,v2,v3,v4,v5}；
Each element V of the comment set V1，v2，v3，v4，v5One-to-one correspondence evaluation criterion score of 0, 25, 50, 75, 100 is expressed as the evaluation criterion scoring matrix S ═ 0255075100]。
9. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 8, wherein in the step S3.3, the third-level evaluation index u iskliWith respect to elements V of comment set V1，v2，v3，v4，v5Membership function u ofkliv1(x)、ukliv2(x)、ukliv3(x)、ukliv4(x)、ukliv5(x) The mathematical expressions of (a) are respectively:
Figure FDA0002570156500000041
Figure FDA0002570156500000042
Figure FDA0002570156500000043
wherein C iskli1、Ckli2、Ckli3、Ckli4、Ckli5Respectively represents ukliAbout v1，v2，v3，v4，v5Has an evaluation criterion threshold value of Ckli1＜Ckli2＜Ckli3＜Ckli4＜Ckli5。
10. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem of claim 8, wherein the step S4 comprises the following steps:
step S4.1: according to the index weight matrix of each layer
Figure FDA0002570156500000044
W*And fuzzy discrimination matrix r of third-level evaluation indexkliSuccessively calculating fuzzy discrimination matrix r of second-level evaluation indexklFuzzy discrimination matrix r of first-level evaluation indexkA fuzzy discrimination matrix r of potential risks of the ecological environment quality of the coastal wetland;
step S4.2: fuzzy discrimination matrix r according to evaluation indexes of all levelskl、rkR and an evaluation criterion submatrix S are used for respectively calculating potential risk indexes p ═ r at each stageklST、p＝r1ST、p＝rSTIn which S isTTransposing for S;
step S4.3: respectively determining the potential risk grades of all levels of evaluation indexes according to the potential risk indexes p: when p is more than or equal to 0 and less than or equal to 12.5, the potential risk is first grade; when p is more than 12.5 and less than or equal to 37.5, the potential risk is second grade; when p is more than 37.5 and less than or equal to 62.5, the potential risk is three levels; when p is more than 62.5 and less than or equal to 87.5, the potential risk is four levels; if p is more than 87.5 and less than or equal to 100, the potential risk is five grades.
11. The method for evaluating the potential risk of the environmental quality of the coastal wetland ecosystem according to claim 10, wherein the step S4.1 comprises the following steps:
step S4.1.1: evaluating the index u for each second levelklAccording to u, respectivelyklThird-level evaluation index ukliIs constructed by the fuzzy discriminant matrix rkliConstructing a single-factor evaluation matrix R thereofkl，
Figure FDA0002570156500000051
And combining the third-level evaluation index ukliFor the second level evaluation index uklOf importanceWeight vector
Figure FDA0002570156500000052
Calculating its fuzzy discriminant matrix
Figure FDA0002570156500000053
Step S4.1.2: evaluating the index u for each first levelkAccording to u, respectivelykSecond-stage evaluation index uklFuzzy discrimination matrix r ofkiConstructing a single-factor evaluation matrix R thereofk，
Figure FDA0002570156500000054
And combining the second evaluation index uklFor the first-order evaluation index ukWeight vector of importance
Figure FDA0002570156500000055
Calculating its fuzzy discriminant matrix
Figure FDA0002570156500000056
Step S4.1.3: according to the first-level evaluation index ukFuzzy discrimination matrix r ofkConstructing a single-factor evaluation matrix R for evaluating the potential risk of the ecological environment quality of the coastal wetland,
Figure FDA0002570156500000057
and combining the first evaluation index ukWeight vector W for evaluating importance of potential risks of coastal wetland ecological environment quality*Calculating a fuzzy discrimination matrix r ═ W for evaluating potential risks of the ecological environment quality of the coastal wetland*·R。
CN202010637865.4A 2020-07-06 2020-07-06 Method for evaluating potential risk of environmental quality of coastal wetland ecosystem Pending CN111832927A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010637865.4A CN111832927A (en) 2020-07-06 2020-07-06 Method for evaluating potential risk of environmental quality of coastal wetland ecosystem

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010637865.4A CN111832927A (en) 2020-07-06 2020-07-06 Method for evaluating potential risk of environmental quality of coastal wetland ecosystem

Publications (1)

Publication Number Publication Date
CN111832927A true CN111832927A (en) 2020-10-27

Family

ID=72900184

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010637865.4A Pending CN111832927A (en) 2020-07-06 2020-07-06 Method for evaluating potential risk of environmental quality of coastal wetland ecosystem

Country Status (1)

Country Link
CN (1) CN111832927A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105023067A (en) * 2015-08-04 2015-11-04 环境保护部南京环境科学研究所 Analytic hierarchy process-fuzzy comprehensive evaluation-based chemical project environmental risk evaluation system
CN109934490A (en) * 2019-03-13 2019-06-25 广州海洋地质调查局 A kind of seashore wetland Eco-Geo-Environment overall merit and grade partition method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105023067A (en) * 2015-08-04 2015-11-04 环境保护部南京环境科学研究所 Analytic hierarchy process-fuzzy comprehensive evaluation-based chemical project environmental risk evaluation system
CN109934490A (en) * 2019-03-13 2019-06-25 广州海洋地质调查局 A kind of seashore wetland Eco-Geo-Environment overall merit and grade partition method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
吴婧;: "基于层次分析法的供水突发事件应急演练模糊综合评估", 城镇供水, no. 01 *
崔正国；龚蓉珠；陈碧鹃；马绍赛；夏斌苑；旭洲；: "基于模糊层次分析的栉孔扇贝食用安全环境风险评价", 中国渔业质量与标准, vol. 2, no. 2, pages 1 - 2 *
常喜梅;李磊磊;: "基于模糊综合评估法的顶管施工沉降风险评估", 现代交通技术, no. 03 *
申海燕;陈妍哲;张盈;: "基于层次分析-模糊综合评价法对城市内涝风险评估研究", 科技资讯, no. 02 *

Similar Documents

Publication Publication Date Title
Jost et al. Differentiation measures for conservation genetics
Benardos et al. Optimizing feedforward artificial neural network architecture
CN107146009B (en) Water supply pipe network operation state evaluation method
Güçlü et al. Artificial neural network modelling of a large-scale wastewater treatment plant operation
CN107895100B (en) Drainage basin water quality comprehensive evaluation method and system
CN111291937A (en) Method for predicting quality of treated sewage based on combination of support vector classification and GRU neural network
CN108469507B (en) Effluent BOD soft measurement method based on self-organizing RBF neural network
Nadiri et al. Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning
CN107025338A (en) A kind of sludge bulking fault identification method based on Recurrent RBF Neural Networks
CN112132333A (en) Short-term water quality and water quantity prediction method and system based on deep learning
Salami et al. Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers)
CN111832895B (en) Coastal wetland ecological vulnerability potential risk assessment method based on gray weighted relevance
CN110610308A (en) Method for evaluating environmental technology based on benchmarking method
CN113688458B (en) Foundation scheme optimization method based on analytic hierarchy process
Meng et al. A framework for group decision making with multiplicative trapezoidal fuzzy preference relations
Han et al. Prediction of sludge bulking using the knowledge-leverage-based fuzzy neural network
CN113962164B (en) Organic gas leakage diffusion real-time intelligent early warning method considering uncertainty reasoning
Farrokhi et al. A new framework for evaluation of rainfall temporal variability through principal component analysis, hybrid adaptive neuro-fuzzy inference system, and innovative trend analysis methodology
CN106874686A (en) Index network of personal connections construction method
CN102073952A (en) Water resource bearing capacity evaluation method
Lotfi et al. A novel stochastic wastewater quality modeling based on fuzzy techniques
Pai et al. Predicting performance of grey and neural network in industrial effluent using online monitoring parameters
Civelekoglu et al. Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network
Keybondorian et al. Application of ANFIS-GA algorithm for forecasting oil flocculated asphaltene weight percentage in different operation conditions
CN107220756A (en) A kind of Development of IT-based Education horizontal zone contrast difference analysis method and system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination