Similar articles

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.

[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).