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CLC number: TP183

On-line Access: 2012-04-06

Received: 2011-01-27

Revision Accepted: 2011-08-29

Crosschecked: 2012-03-09

Cited: 8

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Journal of Zhejiang University SCIENCE B 2012 Vol.13 No.4 P.327-334

10.1631/jzus.B1100031


Application of biomonitoring and support vector machine in water quality assessment


Author(s):  Yue Liao, Jian-yu Xu, Zhu-wei Wang

Affiliation(s):  Institute of Information Science and Technology, Ningbo University, Ningbo 315211, China; more

Corresponding email(s):   xujianyu@nbu.edu.cn

Key Words:  Water assessment, Behavioral feature parameter, Support vector machine (SVM), Genetic algorithm (GA), Water quality classification


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Yue Liao, Jian-yu Xu, Zhu-wei Wang. Application of biomonitoring and support vector machine in water quality assessment[J]. Journal of Zhejiang University Science B, 2012, 13(4): 327-334.

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T1 - Application of biomonitoring and support vector machine in water quality assessment
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Abstract: 
The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC50) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu2+, Hg2+, Cr6+, and Cd2+) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.

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