CN106874960A - Based on the fisheries environment class of pollution appraisal procedure for improving SVMs - Google Patents

Based on the fisheries environment class of pollution appraisal procedure for improving SVMs Download PDF

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CN106874960A
CN106874960A CN201710122690.1A CN201710122690A CN106874960A CN 106874960 A CN106874960 A CN 106874960A CN 201710122690 A CN201710122690 A CN 201710122690A CN 106874960 A CN106874960 A CN 106874960A
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崔正国
曲克明
赵俊
陈碧鹃
陈聚法
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Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
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Abstract

A kind of fisheries water environmental pollution grade appraisal procedure based on improvement SVMs.The invention belongs to environmental evaluation technical field, its the step of is the correlative factor for analyzing influence fisheries environment quality in marine engineering first, then evaluation index is built according to correlative factor, and for index sample data adds label, then evaluation index is combined with supporting vector machine model, carry out Support Vector Machines Optimized model with reference to grid search and cross validation method, establish the complex nonlinear relation between evaluation index and fisheries environment credit rating, make evaluation result more scientific and reasonable, meet objective reality, preferable environmental quality grade assessment can be also obtained in the case of Finite Samples.

Description

Based on the fisheries environment class of pollution appraisal procedure for improving SVMs
Technical field
The invention belongs to Environmental Quality Evalution technical field, more particularly to a kind of fishery water based on improvement SVMs Domain environmental pollution grade appraisal procedure.
Technical background
Because marine engineering is in process of construction, substantial amounts of suspension bed sediment can be produced, and poisonous having of containing in suspension Evil material may dissolution produces secondary pollution again in the seawater, have impact on the seawater quality in construction marine site and surrounding waters and sinks Product environment, and then have impact on the existence of fishery aquatile.Therefore to relating to the fisheries environment investigation before and after extra large engineering construction And environmental pollution grade assessment is carried out, and marine environment situation of change before and after construction can be fully grasped, provided for follow-up fishery Source and ecological environmental protection are significant.
The grade assessment of Marine fishery eco-environment quality is influenceed by many factors, dirty for fisheries environment in relating to fisherfolk's journey The grade evaluation studies of dye, be more by the way that some Mathematical Modelings are quantitative or qualitative analysis obtains the infringement of fishery, and this Invention considers the briny environment of reflection Marine fishery eco-environment pollution level and the indices of depositional environment, introduces engineering Supporting vector machine model in learning method is merged to the evaluation index of reflection seawater quality, depositional environment situation, is established Complex nonlinear relation between evaluation index and fisheries environment credit rating, while the finiteness for solving sample collection is asked Topic.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of based on fisheries environment pollution for improving SVMs etc. Level appraisal procedure, the multiple reflection seawater qualities of methods described fusion, the index of depositional environment and improvement supporting vector machine model, carries The quick degree and accuracy of fisheries environment class of pollution assessment high, with engineering application value higher.
The present invention is achieved by the following technical solution：
A kind of fisheries environment class of pollution appraisal procedure based on improvement SVMs, it is comprised the following steps：
A kind of fisheries environment class of pollution appraisal procedure based on improvement SVMs, it is comprised the following steps：
1) investigate and gather the index for reflecting seawater quality and depositional environment before and after marine engineering is implemented, be deposited into data In storehouse；
2) each achievement data is pre-processed using ZCA albefactions；
3) according to《Sea water quality standard》In water grade regulation and computing formula, be index sample data addition correspondence Water grade label；
Further, the step 3) in computing formula be AIt is comprehensive=AIt is organic+AIt is poisonous+AIt is inorganic+AOther；AIt is organic=petroleum-type+suspension Material+floating substance+organic carbon；AIt is poisonous=(chromium+cadmium+lead+copper+mercury+arsenic)/5；AIt is inorganic=inorganic nitrogen+reactive phosphate+vulcanization Thing；AOther=salinity+dissolved oxygen+COD+pH.
4) the achievement data matrix of the 3) tape label that step is obtained, is randomly divided into training set, test set and cross validation collection, Optimal parameter combination C and g is searched in cross validation collection using grid search and right-angled intersection verification method, then training Collection is input to the improvement supporting vector machine model training of setting optimized parameter, obtains class of pollution assessment models, recently enters survey Examination collection test, obtains the accuracy of fisheries environment class of pollution assessment, demonstrates the validity of institute's extracting method.
In above-mentioned steps 1) in：The factor of influence fisheries environment pollution mainly has：The suspension that marine engineering is produced in building Silt, and the poisonous and harmful substance brought by suspension bed sediment.Therefore water environment, deposition ring after investigation marine engineering is implemented Border, wherein water environment survey item are salinity, pH, dissolved oxygen, floating substance, suspended material, inorganic nitrogen, reactive phosphate, change Oxygen demand totally 8 indexs are learned, depositional environment includes sulfide, organic carbon, petroleum-type, heavy metal.Further, described inorganic nitrogen Including nitrate, nitrite and ammonia nitrogen；Described heavy metal includes chromium, cadmium, lead, copper, mercury and arsenic.
In above-mentioned steps 2) in：Because the dimension and span of each index in sample set are differed, while considering Independence between each index, the correlation between index should be as small as possible, can so reduce the training error of model.Cause This uses ZCA whitening techniques preprocessed datas.
In above-mentioned steps 3) in：According to《Sea water quality standard》With《Marine sediment quality standard》In credit rating rule Fixed and computing formula (1), is that index sample data adds corresponding water pollution grade label.
In above-mentioned steps 4) in：The data set by pretreatment, according to being randomly divided into training set and test set and intersection Checking collection, optimal parameter combination C and g is searched using grid search and right-angled intersection verification method in cross validation collection, Training set is input to the improvement supporting vector machine model training of setting optimized parameter, obtains class of pollution assessment models, last defeated Enter test set test, obtain the accuracy of fisheries environment class of pollution assessment, demonstrate the validity of institute's extracting method.
The present invention solves the problems, such as that environmental index sample is small and the difficult fusion of each index, and people is set up using SVMs Work model of mind realizes the assessment of environmental pollution grade.During supporting vector machine model is built, calculated using grid search Method is supported the optimization of vector machine inner parameter, sets up the fisheries environment class of pollution assessment models based on Optimal Parameters, compares With traditional fisheries environment class of pollution appraisal procedure, the present invention is with accuracy higher and intelligent.
Brief description of the drawings
Fig. 1 is based on improving SVMs to fisheries environment class of pollution appraisal procedure flow chart；
Fig. 2 SVC (Support Vector Classification) parameter selects grid optimization contour map；
Fig. 3 SVC parameters selection optimization grid graphics.
Specific embodiment
Technical scheme is described in detail with case study on implementation below in conjunction with the accompanying drawings, Fig. 1 gives ocean The evaluation process of fisheries environment pollution level, comprises the following steps that：
Step 1：Marine fishery eco-environment pollution level analysis of Influential Factors.
Influenceing the factor of fisheries water environmental quality mainly has：The suspension bed sediment that marine engineering is produced in building, Yi Jiyou The poisonous and harmful substance that suspension bed sediment brings.Therefore water environment, depositional environment after investigation marine engineering is implemented, wherein water environment Survey item includes salinity, pH, dissolved oxygen, floating substance, suspended material, inorganic nitrogen, reactive phosphate, COD totally 8 Item index, depositional environment includes sulfide, organic carbon, petroleum-type, heavy metal.Further, described inorganic nitrogen include nitrate, Nitrite and ammonia nitrogen；Described heavy metal includes chromium, cadmium, lead, copper, mercury and arsenic.
Step 2：The pretreatment of evaluation index data.
Because the dimension and span of each index in sample set are differed, while in view of the independence of each index Property, the correlation between index should be as small as possible, can so reduce the training error of model.Therefore it is pre- using ZCA whitening techniques Processing data.
Step 3：For index sample data adds water pollution grade label.
According to《Sea water quality standard》With《Marine sediment quality standard》In credit rating regulation and computing formula (1) it is, that index sample data adds corresponding environmental pollution grade label.
Step 4：With reference to achievement data and supporting vector machine model, fisheries environment class of pollution evaluation model is obtained.
The data set by pretreatment, according to training set and test set and cross validation collection is randomly divided into, using grid Search and right-angled intersection verification method search optimal parameter combination C and g in cross validation collection, and training set is input to setting The improvement supporting vector machine model training of optimized parameter, obtains class of pollution assessment models, recently enters test set test, obtains The accuracy of fisheries environment class of pollution assessment, carrys out the quality of assessment models.
Described step 1 is described as follows：
The factor of influence fishery marine environment pollution mainly has：The suspension such as sandstone in marine engineering construction, and by hanging The harmful substances such as the heavy metal that mud scum sand ribbon is come.In order to reflect seawater quality, Sediment environment quality condition, structure of the present invention comprehensively Following index system is built.It is as shown in Table 1 below：
The influence factor and index system of the fisheries environment pollution level of table 1
In the evaluation index of Marine fishery eco-environment pollution level, suspended material is one of important observation index, and it contains The size of amount determines pollution level of the marine engineering to briny environment, and it is below sample data addition label emphasis consideration to be also Index.Marine engineering sea fishery is caused potential impact other virulence factors include sulfide, petroleum-type, heavy metal.
Described step 2 is described as follows：
Present case uses the Monitoring Data in Bohai Offshore bay, because the dimension difference between parameters is larger, because This is normalized to sample, by effectively have adjusted the scope of index and the difference of dimension after pretreatment, it is to avoid numerical value The appearance of improper situation is accepted or rejected, precision of prediction is improved.Make the standard of each index to reduce the redundancy of achievement data simultaneously It is unified so that all of property variance is consistent, uncorrelated or correlation is relatively low as far as possible between different attribute.Therefore in sample number According to entering whitening processing to initial data before being input to SVMs.Had the following properties that through the input data after whitening processing：
1) correlation is relatively low between feature；
2) all features have identical variance.
Common whitening operation has PCA (Principal Component Analysis) albefactions and ZCA albefactions, wherein PCA albefactions can ensure that the variance of each dimension of data is 1, and can be used for dimensionality reduction can also decorrelation；ZCA albefactions are to ensure that data are each The variance of dimension is identical, is mainly used in decorrelation, and make the data after albefaction close to original input data as far as possible.Assuming that original Achievement data is concentrated with n sample, and each sample has d index, and ZCA whitening process is as follows：
1) zero averaging first is carried out to original index matrix X before using albefaction, this is subtracted with per one-dimensional sample data The average of dimension is worth to Xi, sample data value is 0-1 after normalization, so as to obtain normalization matrix A.
2) the 2nd requirement according to albefaction, is to reduce the correlation between feature first, is utilized The corresponding sample covariance matrix ∑s of calculating matrix A, and singular value decomposition is done to covariance matrix, obtain corresponding characteristic value λ1>λ2>…λnWith corresponding eigenvectors matrix U, wherein U=[u1,…,un], then using eigenvectors matrix U as base vector New sample data X is obtained after conversionrot, wherein Xrot=UTX。
3) in order that postrotational each input pointer has unit variance, directly useContracted as zoom factor Put each index Xrot,i, now obtain PCA whitening processing results
4) ZCA albefactions are that a rotation process has been done on the basis of PCA albefactions so that the data after albefaction are more nearly Initial data, by XPCAwhite,iPremultiplication matrix U obtains the result X after initial data ZCA albefactionsZCAwhite,i=UXPCAwhite,i。
During actually whitening processing is carried out, it may appear that some eigenvalue λsiNumerically close to 0 situation, this meeting Cause to occur being approximately equal to 0 divided by one during scalingData overflow or the unstable phenomenon of numerical value are in turn resulted in, In order to overcome these problems, realize scaling process using regularization in actual applications, i.e., make even root and inverse before to Characteristic value adds a constant ε for very little, finally utilizesProcessed, usual ε goes one very Small positive number, the inventive method ε=0.1 due to there is negative in the data after albefaction, therefore uses minimax normalizing again Change data are 0-1.
The sub-step of described step 3 is described as follows：
S3.1：The determination of Marine fishery eco-environment pollution level grade scale
The present invention evaluate Marine fishery eco-environment pollution level when, according to GB《Sea water quality standard》With《Ocean sinks Product amount of substance standard》In regulation, show that sea fishery water pollution degree can be divided into six grades, it is as shown in Table 2 below.
The Marine fishery eco-environment comprehensive pollution indexes grade classification of table 2
According to the Marine fishery eco-environment quality evaluation computing formula that pertinent literature is proposed, AIt is comprehensive=AIt is organic+AIt is poisonous+AIt is inorganic+AOther (1) A in formulaIt is comprehensiveIt is seawater quality comprehensive pollution indexes, AIt is organic、AIt is poisonous、AIt is inorganic、AOtherRespectively organic contamination index, toxic pollutant Index, inorganic matter index and other monitoring indexes；AIt is organic=petroleum-type+suspended material+floating substance+organic carbon；AIt is poisonous=(chromium+ Cadmium+lead+copper+mercury+arsenic)/5；AIt is inorganic=inorganic nitrogen+reactive phosphate+sulfide；AOther=salinity+dissolved oxygen+COD+ pH.The desired value that above-mentioned formula is mentioned is standardized data.
S3.2：It is index sample addition environmental pollution grade label according to the monitoring index of each index
According to the data after standardization, corresponding formula (1) is updated to, is divided into according to complex pollution indices of environmental quality Corresponding grade, the achievement data after which part normalization is shown in Table 3, and wherein sample sequence number 1-4 is slight pollution, 4-8 It is serious pollution.
The part sample index data of table 3
Described step 4 is described as follows：
S4.1：SVMs is mainly based upon following thought：By the Nonlinear Mapping of prior selection by input vector High-dimensional feature space is mapped to, optimal decision function is constructed in this space.When optimal decision function is constructed, using finishing Structure principle of minimization risk, while introducing the concept at interval.And the kernel function using former space instead of high-dimensional feature space Dot-product operation, it is to avoid complicated calculations.Current algorithm of support vector machine mainly has classification and recurrence etc..The present invention uses non-thread Property sorting algorithm.
If original index data are concentrated with n sample, each sample has d index, by Nonlinear MappingSample (x1,y1),…(xn,yn) it is mapped to high-dimensional feature spaceIn this high-dimensional feature space Middle construction hyperplaneSo Nonlinear Classification is converted into linear in high-dimensional feature space Classification, that is, meetingUnder conditions of (ζiIt is to allow the wrong slack variable divided), minimize | | w | | problem, optimization problem is：
ζi>=0, i=1 ..., n, c are given constant.
This optimization problem is solved using Lagrangian method:
Wherein αi,riIt is Lagrange multiplier αi≥0,ri>=0, i=1 ..., n.
According to optimal conditions：
Obtain：
(5) are substituted into (3), problem is converted into maximization expression
The dot product of SVMs not direct solution high-dimensional feature spaceBut with former space Kernel function K (xi,xj) replace it, kernel function K (xi,xj) it is the symmetric function for meeting Mercer conditions.
So optimization problem is converted into, and maximal function W (a) under constraints, according to KT conditions, hyperplane is：
Finally grader is：F (x, w, b)=sgn [∑sSupporting vectorαiyiK(x,xi)+b=0].
S4.2：Kernel function
Selection different kernel function K (x, xi), different vector machines can be constructed, the present invention is considered from radial direction base core Function：
Wherein γ is regulation coefficient, and σ is core width.Combined using grid search and cross validation and search out optimal parameter C And g, wherein C=22.6274, g=0.25, parameter selection result is shown in Fig. 2 and Fig. 3.
Input training set obtains class of pollution assessment models to supporting vector machine model training is improved, and test set is input to Class of pollution assessment models, (wherein 36 31, samples are commented for 86.12% to obtain the accuracy of fisheries environment Contamination Assessment grade Level is correct), corresponding fisheries environment quality-monitoring report is compareed, Contamination Assessment level results are essentially identical with report content, test The feasibility of this method is demonstrate,proved.
In summary analyze, the present invention is by merging multiple Seawater environmental quality indexs for reflecting fisheries environment pollution levels And depositional environment quality index, and ZCA whitening pretreatments are carried out to achievement data matrix, it is input to improvement supporting vector machine model Middle training, has obtained fisheries environment class of pollution assessment models, and the evaluation result of this method is scientific and reasonable, with engineering higher Actual application value.

Claims (4)

1. it is a kind of based on the fisheries environment class of pollution appraisal procedure for improving SVMs, it is characterised in that it includes following step Suddenly：
1) investigate and gather the index for reflecting seawater quality and depositional environment before and after marine engineering is implemented, be deposited into database In；
2) each achievement data is pre-processed using ZCA albefactions；
3) according to《Sea water quality standard》In water grade regulation and computing formula, be that index sample data adds corresponding water Matter grade label；
4) the achievement data matrix of the 3) tape label that step is obtained, is randomly divided into training set, test set and cross validation collection, uses Grid search and right-angled intersection verification method search optimal parameter combination C and g in cross validation collection, then that training set is defeated Enter the improvement supporting vector machine model training to setting optimized parameter, obtain class of pollution assessment models, recently enter test set Test, obtains the accuracy of fisheries environment class of pollution assessment, demonstrates the validity of institute's extracting method.
2. according to claim 1 a kind of based on the fisheries environment class of pollution appraisal procedure for improving SVMs, its It is characterised by the step 1) in：Water environment survey item is salinity, pH, dissolved oxygen, floating substance, suspended material, inorganic Nitrogen, reactive phosphate, COD totally 8 indexs, depositional environment include sulfide, organic carbon, petroleum-type, heavy metal.
3. according to claim 2 a kind of based on the fisheries environment class of pollution appraisal procedure for improving SVMs, its It is characterised by that described inorganic nitrogen includes nitrate, nitrite and ammonia nitrogen；Described heavy metal includes chromium, cadmium, lead, copper, mercury And arsenic.
4. according to claim 1 a kind of based on the fisheries environment class of pollution appraisal procedure for improving SVMs, its Be characterised by the step 3) in computing formula be AIt is comprehensive=AIt is organic+AIt is poisonous+AIt is inorganic+AOther；AIt is organic=petroleum-type+suspended material+drift Float matter+organic carbon；AIt is poisonous=(chromium+cadmium+lead+copper+mercury+arsenic)/5；AIt is inorganic=inorganic nitrogen+reactive phosphate+sulfide；AOther= Salinity+dissolved oxygen+COD+pH.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107357840A (en) * 2017-06-23 2017-11-17 广东开放大学（广东理工职业学院） A kind of fishery big data determination method and system
CN109190979A (en) * 2018-09-03 2019-01-11 深圳市智物联网络有限公司 A kind of industry internet of things data analysis method, system and relevant device
CN111582734A (en) * 2020-05-12 2020-08-25 上海海洋大学 Ocean pollution comparative analysis and risk assessment intelligent method based on python crawler system and SVM
CN112101789A (en) * 2020-09-16 2020-12-18 清华大学合肥公共安全研究院 Water pollution alarm grade identification method based on artificial intelligence
CN112766739A (en) * 2021-01-22 2021-05-07 北京工商大学 BWM-E model-based evaluation method for heavy metal pollution in meat products

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102902985A (en) * 2012-10-10 2013-01-30 常州大学 Coastal water quality evaluation method based on two-classification support vector machines and particle swarm algorithm
CN102999709A (en) * 2012-12-20 2013-03-27 中国环境科学研究院 Underground water grading and zoning evaluation method in agricultural activity area

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102902985A (en) * 2012-10-10 2013-01-30 常州大学 Coastal water quality evaluation method based on two-classification support vector machines and particle swarm algorithm
CN102999709A (en) * 2012-12-20 2013-03-27 中国环境科学研究院 Underground water grading and zoning evaluation method in agricultural activity area

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
冯帅等: ""太湖流域上游河网污染物降解系数研究"", 《环境科学学报》 *
李赢等: ""基于模糊聚类和完全二叉树支持向量机的变压器故障诊断"", 《电工技术学报》 *
李雪等: ""基于BP人工神经网络的海水水质综合评价"", 《海洋通报》 *
纪昌明等: ""基于网格搜索和交叉验证的支持向量机在梯级水电系统隐随机调度中的应用"", 《电力自动化设备》 *
谢霖铨等: ""基于降噪自编码的推荐算法"", 《计算机与现代化》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107357840A (en) * 2017-06-23 2017-11-17 广东开放大学（广东理工职业学院） A kind of fishery big data determination method and system
CN109190979A (en) * 2018-09-03 2019-01-11 深圳市智物联网络有限公司 A kind of industry internet of things data analysis method, system and relevant device
CN111582734A (en) * 2020-05-12 2020-08-25 上海海洋大学 Ocean pollution comparative analysis and risk assessment intelligent method based on python crawler system and SVM
CN112101789A (en) * 2020-09-16 2020-12-18 清华大学合肥公共安全研究院 Water pollution alarm grade identification method based on artificial intelligence
CN112766739A (en) * 2021-01-22 2021-05-07 北京工商大学 BWM-E model-based evaluation method for heavy metal pollution in meat products
CN112766739B (en) * 2021-01-22 2023-08-11 北京工商大学 Method for evaluating heavy metal pollution in meat product based on BWM-E model

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