River flow forecasts are required to provide basic information for reservoir management in a multipurpose water system optimisation framework. An accurate prediction of flow rates in tributary streams is crucial to optimise the management of water resources considering extended time horizons. In the paper different NN approaches will be analyzed to model the rainfall-runoff process when different time step durations have to be considered in reservoir management. Alternative neural models of the rainfall-runoff process are presented and discussed. Some numerical results are provided for runoff prediction in the Tirso basin at the S.Chiara section in Sardinia (Italy), using combinations of area and point-based measurements.

River Flow Forecast for Reservoir Management Using Neural Networks

FANNI, ALESSANDRA;CANNAS, BARBARA;SECHI, GIOVANNI MARIA
2001

Abstract

River flow forecasts are required to provide basic information for reservoir management in a multipurpose water system optimisation framework. An accurate prediction of flow rates in tributary streams is crucial to optimise the management of water resources considering extended time horizons. In the paper different NN approaches will be analyzed to model the rainfall-runoff process when different time step durations have to be considered in reservoir management. Alternative neural models of the rainfall-runoff process are presented and discussed. Some numerical results are provided for runoff prediction in the Tirso basin at the S.Chiara section in Sardinia (Italy), using combinations of area and point-based measurements.
0-867405252
Neural Networks, Rainfall-runoff process, Flow prediction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/11778
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