Linearity versus non-linearity in forecasting Nile river flows

Abstract

Egypt is almost totally dependent on the Nile River for satisfying about 95% of its water requirements. Aswan High Dam (AHD), located at the most upstream point of the fiver controls Egypt's share of water. Once a release decision is made, there is no chance of retrieving or recovering this released water. Therefore, long- and short-term forecasts of Nile flows at Aswan have been recognized to be of great importance to allow better management and operation of the reservoir.Several autoregressive (AR) models of uni- or multi-site flows upstream of Aswan had been developed to forecast monthly reservoir inflows for some lead-time. Most of these models failed to forecast, with satisfactory accuracy, the peak flows of July, August, and September due to high variability of flows during these months. Some hydrologists contributed this inaccuracy to the linearity assumption embedded in AR models.Artificial neural networks (ANNs) are being tested as a forecast tool to consider the non-linearity. Several neural networks using NeuralystTM software have been investigated against updated AR models. The results indicated that the inclusion of non-linearity in the ANNs forecast might in some cases lead to improved forecast accuracy.