Artificial neural network model for river flow forecasting in a developing country

Abstract

The present paper deals with exploring the use of Artificial Neural Networks (ANN) for forecasting the Blue Nile river flows in Sudan. Four ANN rainfall–runoff models based on the structure of the well-known multi-layer perceptron are developed. These models use the rainfall index as a common external input, with the rainfall index being a weighted sum of the recent and current rainfall. These models differ in terms of the additional external inputs being used by the model. The additional inputs are basically the seasonal expectations of both the rainfall index and the observed discharge. The results show that the model, which uses both the seasonal expectation of the observed discharge and the rainfall index as additional inputs, has the best performance. The estimated discharges of this model are further updated using a non-linear Auto-Regressive Exogenous-input model (NARXM)-ANN river flow forecasting output-updating procedure. In this way, a real-time river flow forecasting model is developed. The results show that the forecast updating has significantly enhanced the quality of the discharge forecasts. The results also indicate that the ANN has considerable potential to be used for river flow forecasting in developing countries.