River flow forecast for reservoir management through neural networks

River ow forecasts are required to provide basic information for reservoir management in a multipurpose water system optimisation framework. An accurate prediction of ow rates in tributary streams is crucial to optimise the management of water resources considering extended time horizons. Moreover, runo3 prediction is crucial in protection from water shortage and possible ood damages. In this paper, a neural approach is used to model the rainfall-runo3 process when di3erent time step durations have to be considered in reservoir management. Numerical comparisons with observed data are provided for runo3 prediction in the Tirso basin at the S.Chiara section in Sardinia (Italy). c © 2003 Elsevier B.V. All rights reserved.