Full Text:   <3521>

CLC number: TP183

On-line Access: 2012-04-06

Received: 2011-01-27

Revision Accepted: 2011-08-29

Crosschecked: 2012-03-09

Cited: 8

Clicked: 6097

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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


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

Share this article to: More <<< Previous Article|

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",
publisher="Zhejiang University Press & Springer",

%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

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

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Andre, M., 2003. Multivariate analysis and classification of the chemical quality of 7-aminocephalsporanic acid using near-infrared reflectance spectroscopy. Anal. Chem., 75(14):3460-3467.

[2]Darwin, C.R., 1869. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life, 5th Ed. John Murray, London, p.91-92.

[3]Davis, L.D., 1991. Handbook of Genetic Algorithm. van Nostrand Reinhold, New York, p.13-14.

[4]Duan, K.B., Keerthi, S.S., Poo, A.N., 2003. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing, 51(0):41-59.

[5]Gerlai, R., 2003. Zebra fish: an uncharted behavior genetic model. Behav. Genet., 33(5):461-468.

[6]Huang, C.L., Wang, C.J., 2006. A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl., 31(2):231-240.

[7]Israeli-Weinstein, D., Kimmel, E., 1998. Behavioral response of carp (Cyprinus carpio) to ammonia stress. Aquaculture, 165(1-2):81-93.

[8]Kane, A.S., Salierno, J.D., Gipson, G.T., Molteno, T.C.A., Hunter, C., 2004. A video-based movement analysis system to quantify behavioral stress responses of fish. Water Res., 38(18):3993-4001.

[9]Nogita, S., Baba, K., Yahagi, H., Watanabe, S., Mori, S., 1988. Acute Toxicant Warning System Based on a Fish Movement Analysis by Use of AI Concept. Artificial Intelligence for Industrial Applications. IEEE AI '88, Proceedings of the International Workshop. 25-27 May, Hitachi, Japan, p.273-276.

[10]Palani, S., Liong, S.Y., Tkalich, P., 2008. An ANN application for water quality forecasting. Mar. Pollut. Bull., 56(9):1586-1597.

[11]Singh, K.P., Basant, A., Malik, A., Jain, G., 2009. Artificial neural network modeling of the river water quality— a case study. Ecol. Model., 220(6):888-895.

[12]Thomas, M., Florion, A., Chretien, D., Terver, D., 1996. Real-time biomonitoring of water contamination by cyanide based on analysis of the continuous electric signal emitted by a tropical fish: Apteronotus albifrons. Water Res., 30(12):3083-3091.

[13]van der Schalie, W.H., Shedd, T.R., Knechtges, P.L., Widder, M.W., 2001. Using higher organisms in biological early warning systems for real-time toxicity detection. Biosens. Bioelectron., l6(7-8):457-465.

[14]Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York, p.157-173.

[15]Wu, C.H., Tzeng, G.H., Goo, Y.J., Fang, W.C., 2007. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst. Appl., 32(2):397-408.

[16]Xie, L.J., Ye, X.Q., Liu, D.H., Ying, Y.B., 2008. Application of principal component-radial basis function neural network (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy. J. Zhejiang Univ.-Sci. B, 9(12):982-989.

[17]Xu, J.Y., Liu, Y., Cui, S.R., Miao, X.W., 2006a. Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved oxygen levels as monitored by computer vision. Aquac. Eng., 35(3):207-217.

[18]Xu, J.Y., Jiang, X.H., Liu, Y., 2006b. Quantifying the fish skin darkness using computer vision. J. Agric. Res., (6):140-142 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE