Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation

Abstract

The majority of artificial neural network (ANN) applications to water resources data employ the feed-forward back-propagation (FFBP) method. This study used an ANN algorithm, the generalized regression neural network (GRNN), for intermittent river flow forecasting and estimation. GRNNs were superior to FFBP in terms of the selected performance criteria. The GRNN simulations do not face the frequently encountered local minima problem in FFBP applications, and GRNNs do not generate forecasts or estimates that are not physically plausible. Preliminary analysis of statistics such as auto- and cross correlation, which explained variance by multilinear regression and the Akaike criterion for the autoregressive moving average (ARMA) model of corresponding order, were found quite informative in determining the number of nodes in the input layer of neural networks.