High precision radial basis function network model for predicting water environment nonlinear time series

In order to raise the precision of prediction model for water environment nonlinear time series,a high precision radial basis function(RBF) artificial neural network model is presented The delay time of water environment time series is analyzed with auto correlation technique.The input and output of this model are decided by this delay time.And this model is verified by two cases First,the training and test are given by the recorded data of 32 years of marine water temperature,and the result shows that the error of every training sample is 0.00,and the relative error is 0.3875% of forecasting marine temperature based on the RBF network model.Then,the water environment elements,such as hardness,the salts of permanganic acid,BOD₅,NH₃-N,DO,phenol,cadmium,chloral and sulfate,are predicted at Wangjianglou in Changjiang river for eight years.The prediction precision of this RBF model is higher than that of the threshold auto-regression model,the BP artificial neural network model and the ELMAN artificial neural network model.The convergent speed of this new model is also faster than that of the BP model It is a good nonlinear prediction model for water science time series.