CLC number: TP183
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2012-03-09
Cited: 8
Clicked: 6568
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.
@article{title="Application of biomonitoring and support vector machine in water quality assessment",
author="Yue Liao, Jian-yu Xu, Zhu-wei Wang",
journal="Journal of Zhejiang University Science B",
volume="13",
number="4",
pages="327-334",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1100031"
}
%0 Journal Article
%T Application of biomonitoring and support vector machine in water quality assessment
%A Yue Liao
%A Jian-yu Xu
%A Zhu-wei Wang
%J Journal of Zhejiang University SCIENCE B
%V 13
%N 4
%P 327-334
%@ 1673-1581
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1100031
TY - JOUR
T1 - Application of biomonitoring and support vector machine in water quality assessment
A1 - Yue Liao
A1 - Jian-yu Xu
A1 - Zhu-wei Wang
J0 - Journal of Zhejiang University Science B
VL - 13
IS - 4
SP - 327
EP - 334
%@ 1673-1581
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1100031
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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