Counterpropagation fuzzy-neural network for streamflow reconstruction

This study combines neural networks and fuzzy arithmetic to present a count erpropagation fuzzy-neural network (CFNN) for streamflow reconstruction. Th e CFNN has a rule-based control, a modified self-organizing counterpropagat ion network, and a fuzzy control predictor. It can generate rules automatic ally by increasing the training data to improve the accuracy or streamflow reconstruction. The CFNN establishes the input and output relationship of a watershed without set-up parameters. The parameters are estimated systemat ically by the approach converging to an optimal solution. One sequence of d ata generated by the Monte Carlo method is used to demonstrate the accuracy of the CFNN. The streamflow data of the Da-chia River, in central Taiwan, is also used to evaluate the performances of the CFNN. The results indicate the reliability and accuracy of the CFNN for streamflow reconstruction. Co pyright (C) 2001 John Wiley & Sons, Ltd.