Single neural network and neuro-updating conceptual model for forecasting runoff Online publication date: Tue, 27-Sep-2016
by Tayeb Boulmaiz; Lahbassi Ouerdachi; Djamel Boutoutaou; Hamouda Boutaghane
International Journal of Hydrology Science and Technology (IJHST), Vol. 6, No. 4, 2016
Abstract: Application of artificial neural network (ANN) becomes an alternative or complementary technique for forecasting runoff, which is an important operation for water management or flood protection. Two using approaches of ANN are applied in this study. The first is to use a nonlinear autoregressive with exogenous inputs network (NARXN) as a single model with rainfall data as inputs to forecast runoff, while the second is to use a feedforward ANN to update the outputs of a global conceptual model (GR4J). Trial and error procedure has been used for both ANNs in order to search combinations that give the best accuracy. The results show that the second approach performs better for forecasting daily flow (mean square error in test period = 0.19) and is more efficient in terms of water resources with an annual mean supply error of 0.56 Hm3 compared to NARXN model (5.29 Hm3).
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