Title: Single neural network and neuro-updating conceptual model for forecasting runoff

Authors: Tayeb Boulmaiz; Lahbassi Ouerdachi; Djamel Boutoutaou; Hamouda Boutaghane

Addresses: Hydraulic and Hydraulic Constructions Laboratory, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria; Laboratory of Operating and Valorization of Natural Resources in Arid Areas (LVRENZA), Kasdi Merbah University, BP No. 102 El Khefdji, rue de Ghardaïa, Ouargla, Algeria ' Hydraulic and Hydraulic Constructions Laboratory, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria ' Laboratory of Operating and Valorization of Natural Resources in Arid Areas (LVRENZA), Kasdi Merbah University, BP No. 102 El Khefdji, rue de Ghardaïa, Ouargla, Algeria ' Hydraulic and Hydraulic Constructions Laboratory, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria

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).

Keywords: Algeria; artificial neural networks; ANNs; conceptual models; runoff forecasting; GR4J; hydrology; NARXN model; Oued Rassoul; output updating; rainfall runoff; water resources; modelling; water management; flood protection.

DOI: 10.1504/IJHST.2016.079344

International Journal of Hydrology Science and Technology, 2016 Vol.6 No.4, pp.344 - 358

Received: 31 Mar 2015
Accepted: 07 Nov 2015
Published online: 27 Sep 2016 *

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