RUNOFF FORECASTING USING RBF NETWORKS WITH OLS ALGORITHM

This paper illustrates the application of the radial basis function type of artificial neural networks (ANNs) using the orthogonal least-squares (OLS) algorithm to model the rainfall runoff process. Models using this approach differ from the more commonly used ANN models that adopt the back propagation (BP) algorithm, in that the former are linear in the parameters. The OLS algorithm is also capable of synthesizing the suitable network architecture, relieving the user of a time-consuming trial-and-error procedure. The proposed method is then applied to forecast runoff in a small catchment. One-hour predictions using the model are compared with those predicted by an ANN model that uses the BP algorithm, an ARMAX model, and with observed values. Results indicate that the OLS algorithm-based approach produces forecasts of comparable accuracy to those based on the BP algorithm. This approach has the added advantage of requiring less time for model development, and is also readily usable by the hydrologist with little or no background knowledge of ANNs.