Application of radial basis function neural networks to short-term streamflow forecasting

The availability of adequate data is one of several factors that inform the choice of model used in various aspects of water resources research and management. Unfortunately, a number of models developed for use in water resources management find limited application due to their sophisticated data requirements. For a number of catchments in South Africa, the data available is insufficient for the adequate performance of some of these models. Consequently, for such catchments the ideal modeling approach would be one where the data available determines model requirements as opposed to the more conventional approach where the model data requirements are not readily available. For this reason, the use of radial basis function artificial neural networks in performing 1-day forecasts of streamflow was demonstrated in this study. Adept at simulating complex non-linear relationships, their consideration for use in streamflow modeling enabled the forecasting of streamflow in the Luvuvhu River in South Africa based on the data that was readily available. The suitability of the artificial neural network as a tool for streamflow forecasting was assessed by using the Nash–Sutcliffe efficiency (NSE) and the percent bias (% Bias) measures between the simulated and observed data. Over two of the reaches on the river, satisfactory model performance was obtained with the average NSE and % Bias measures of 0.988 and −22.76% respectively achieved during the calibration phase and 0.980 and −20.30% during the verification phase. This study also demonstrated that the RBFN are amenable, through choice of objective function, to forecast with greater accuracy portions of the hydrograph such as the situation may warrant for instance when low flow forecasting is of significance. The results suggest that artificial neural networks hold promise for streamflow forecasting in South Africa.