A COUNTERPROPAGATION FUZZY-NEURAL NETWORK MODELING APPROACH TO REAL TIME STREAMFLOW PREDICTION

Abstract

A counterpropagation fuzzy-neural network (CFNN) is the fusion of a neural network and fuzzy arithmetic. It can automatically generate the rules used for clustering the input data. No parameter input is needed, because the parameters are systematically estimated by the approach of converging to an optimal solution. The advantages of the CFNN include the ability to cluster, learn, and construct, and the model presented herein is used to develop a hydrological model. The CFNN can automatically construct a rainfall-runoff model to forecast streamflow. The available streamflow and precipitation data of the upstream of the Da-cha River, in central Taiwan, is used to evaluate the CFNN rainfall-runoff model. A comparison of the results obtained by the CFNN model and ARMAX indicate the superiority and reliability of the CFNN rainfall-runoff model.