Exploration On Neural Networks For Water Quality Assessment And Prediction

Artificial neural network(ANN) plays a leading role in the sciences for complex non-linear phenomena and artificial intelligence. Researches on its application in water pollution control are still in the preliminary stage in the world. On the basis of a conprehensive Analysis and evaluation of the present situation of the researches in water quality assessment and prediction, and on the bsis of a careful exposition of basic principles and the optimal algorithm of ANN and the basic theory of fuzzy mathematics, this dissertation gives an application of fuzzy neural netowrk (FNN) and radial basis fuction neural network (RBFNN) approach in the sheme of water pollution control, and the main research of this article is to do explore some new approach for water quality assessment and prediction.This paper emphasizes the principles, the algorithm and the pattern features of FNN model that is composed by ANN and Fuzzy System according to learning integrated. The FNN model is not a black box any more and its all nodes and parameters have physical meaning, and it overcomes the disadvantage which choosing ANN configuration is short of sufficient theoretical analysis. FNN model can not only direct expresses the logic meaning of peaple's customs but also have the merits of ANN self-adaptation learning and non-linear expression. Researches on FNN application in the water quality evaluation are preliminary exploration of this article's. Case studies show that FNNis abble to correctly evaluate other samples besides the training samples after learning, thus has better objectiveness, reliability and expression.Based on the algorithm of RBFNN study, this article applies it to water prediction that sets up a RBFNN(back propagation neural network) water predicton model on the training data from Shenzhen river on-line water monitoring station, uses the well trained model to predict the water quality of Shenzhen river from 2006-11-19 to 2006-11-29, and assesses the prediction precision by the monitoring data. The case study shows that the prediction results of RBFNN model have high precision. In order to compare the prediction performance of RBFNN's with BPNN's, this paper sets up a BP-NN water prediction model by the same data, and compares its prediciton result with the RBFNN's. The comparing results show that the predicton outcomes of RBFNN'are evidently preciser than the BP-NN's, and in the process of establishing the both models, the author finds that the RBFNN is also better than BPNN both in convergence speed and outcome stability.This research demonstrates that theirs theoretical feasibility and great practical utility, FNN water quality assessment and RBFNN water quality prediction have good prospects for further development and application.