Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin

A simple reservoir routing scheme is applied to the available hydrometeorlogical data from the Kafue River sub-drainage basin in Zambia to derive flow contributions from the ungauged parts of the basin. The derived flow series and the time series of historic flow measured at the Kafue Hook Bridge (KHB) are separately modelled using artificial neural networks (ANNs). For each of these two flow series, relevant input variables are determined with the help of input—output intercorrelations, where inputs are given to a host of three-layer feedforward back-propagation (FF-BP) ANNs to predict the current, derived flow or KHB flow. A couple of ANN models selected on the basis of defined criteria are then used to forecast the flows at m time steps ahead. To evaluate the forecasting performance of the best ANN models, comparison with best autoregressive moving average models with exogenous inputs, ARMAX, is made. In both cases the ANNs give more robust forecasts over long terms than the ARMAX models, thereby making ANNs a viable alternative in flow forecasting.