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Received: 2006-12-26

Revision Accepted: 2007-04-09

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.9 P.1482~1487


Water quality forecast through application of BP neural network at Yuqiao reservoir

Author(s):  ZHAO Ying, NAN Jun, CUI Fu-yi, GUO Liang

Affiliation(s):  School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China

Corresponding email(s):   zhaoying@hit.edu.cn

Key Words:  Water quality forecast, BP neural network, MATLAB, Graphical User Interfaces (GUI)

ZHAO Ying, NAN Jun, CUI Fu-yi, GUO Liang. Water quality forecast through application of BP neural network at Yuqiao reservoir[J]. Journal of Zhejiang University Science A, 2007, 8(9): 1482~1487.

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author="ZHAO Ying, NAN Jun, CUI Fu-yi, GUO Liang",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Water quality forecast through application of BP neural network at Yuqiao reservoir
%A ZHAO Ying
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%A CUI Fu-yi
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%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1482

T1 - Water quality forecast through application of BP neural network at Yuqiao reservoir
A1 - ZHAO Ying
A1 - NAN Jun
A1 - CUI Fu-yi
A1 - GUO Liang
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 9
SP - 1482
EP - 1487
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A1482

This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified, the reservoir’s 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

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


[1] Chang, T.C., Chao, R.J., 2006. Application of backpropagation networks in debris flow prediction. Engineering Geology, 85(3-4):270-280.

[2] Chen, L.H., Chang, Q.C., Chen, X.G., Hu, Z.D., 2003. Using BP neural network to predict the water quality of Yellow River. Journal of Lanzhou University (Natural Sciences), 39(2):53-56 (in Chinese).

[3] Guo, Z.Y., Chen, Z.Y., Li, L.Q., Song, B.P., Lu, Y., 2001. Artificial neural network and its application in regime prediction of groundwater quality. Journal of East China Normal University (Natural Sciences), (1):84-89 (in Chinese).

[4] Kuo, Y.M., Liu, C.W., Lin, K.H., 2004. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Research, 38(1):148-158.

[5] Kurunç, A., Yürekli, K., Çevik, O., 2005. Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey. Environmental Modelling & Software, 20(9):1195-1200.

[6] Lee, J.H.W., Huang, Y., Dickman, M., Jayawardena, A.W., 2003. Neural network modeling of coastal algal blooms. Ecological Modelling, 159(2-3):179-201.

[7] Li, R.Z., 2006. Advance and trend analysis of theoretical methodology for water quality forecast. Journal of Hefei University of Technology, 29(1):26-30.

[8] Mo, H.F., Gu, A.Y., Zhang, X.Z., Zhang, J.C., 2004. Research on a method of BP neural network in water quality evaluation. Control Engineering of China, 11:9-10,19.

[9] Niu, Z.G., Zhang, H.W., Liu, H.B., 2006. Application of neural network to prediction of coastal water quality. Journal of Tianjin Polytechnic University, 25(2):89-92.

[10] Shu, J., 2006. Using neutral network model to predict water quality. North Environment, 31(1):44-46.

[11] Vandenberghe, V., Bauwens, W., Vanrolleghem, P.A., 2007. Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns. Environmental Modelling & Software, 22(5):725-732.

[12] Wang, Q.H., 2004. Improvement on BP algorithm in artificial neural network. Journal of Qinghai University, 22(3):82-84.

[13] Wu, H.J., Lin, Z.Y., Guo, S.L., 2000. The application of artificial neural networks in the resources and environment. Resources and Environment in the Yangtze Basin, 9(2):237-241 (in Chinese).

[14] Xiang, S.L., Liu, Z.M., Ma, L.P., 2006. Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Science, 24(1):60-62.

[15] Xu, D., Wu, Z., 2002. Neural Network-system Design and Analysis Based on MATLAB6.X. University of Xi’an Electronics Technology Press, Xi’an, p.2 (in Chinese).

[16] Xu, L.J., Xing, J.D., Wei, S.Z., Zhang, Y.Z., Long, R., 2007. Optimization of heat treatment technique of high-vanadium high-speed steel based on back-propagation neural networks. Materials & Design, 28(5):1425-1432.

[17] Zhang, Y.H., 1999. Mastering MATLAB5. Tsinghua University Press, Beijing, p.1-2 (in Chinese).

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