Research Of Eutrophication Classification In West Lake, Hangzhou Using Hopfield Neural Network

This paper applies neural network theory to assessment of water quality, establishing multi-indexes integrated water quality assessment model with Hopfield Neural Network(HNN), in order to explore the spatio-temporal class distribution rules in West Lake.We have established 8 sample spots in West Lake, and collected the aquatic data(2000.1-12) of the lake by routine measurement. According to Grading and Classification Standard of Lake and Reservoir in Communique Weaving Precis of Water Resource of China. Chose Chl-a, TP, TN, COD_Mn,SD as input factors. The eutrophication assessment standard is classified to ten sorts. Established a Hopfield Neural Network with 50 nerves, input ten standard models as training model, then remembered by the network. Input actual samples, the sort which sample belong to will be given by the association function of HNN. This paper analyzed 96 samples in 2000, the result showed that the whole lake belonged to eutrophication standard. In spatial, water quality of L. Xiaonan is the best while that of L.Beili is the worst relatively. In temporal, water quality is better during January to March than June to October.This indicates that Hopfield network model has the ability to analyze the non-linear information in eutrophication processes and evaluate the water quality applausively in West Lake, which can provide scientific basis for environmental management and control measurements.