Combined Method of Chaotic Theory and Neural Networks for Water Quality Prediction

Abstract： Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction,the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance. 作 者： ZHANG Shudong    LI Weiguang    NAN Jun    WANG Guangzhi    ZHAO Lina   作者單位： ZHANG Shudong,NAN Jun,WANG Guangzhi(School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China)

LI Weiguang(School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China;National Engineering Research Center of Urban Water Resources, Harbin 150090, China)

ZHAO Lina(Harbin Bureau of Hydrology, Harbin 150010, China) 

期 刊： 東北農(nóng)業(yè)大學(xué)學(xué)報（英文版）    Journal： JOURNAL OF NORTHEAST AGRICULTURAL UNIVERSITY(ENGLISH EDITION)  年，卷(期)： 2010, 17(1)  分類號： X83  Keywords： water quality prediction    BP neural network    chaotic time series