Geodesic
Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
A. Sedki1 ; D. Ouazar1
1 Department of Civil Engineering, Mohammadia School of Engineering University Mohammed V-Agdal, Rabat, Morocco
Mathematical modelling of natural phenomena, Tome 5 (2010) no. 7 Supplement, pp. 132-138.

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In this paper, an artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) with back-propagation (BP) is proposed to forecast the daily streamflows in a catchment located in a semi-arid region in Morocco. The PSO algorithm has a rapid convergence during the initial stages of a global search, while the BP algorithm can achieve faster convergent speed around the global optimum. By combining the PSO with the BP, the hybrid algorithm referred to as BP-PSO algorithm is presented in this paper. To evaluate the performance of the hybrid algorithm, BP neural network is also involved for a comparison purposes. The results show that the neural network model evolved by PSO-BP algorithm has a good predictions and better convergence performances
DOI : 10.1051/mmnp/20105722

A. Sedki 1 ; D. Ouazar 1

1 Department of Civil Engineering, Mohammadia School of Engineering University Mohammed V-Agdal, Rabat, Morocco 
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A. Sedki; D. Ouazar. Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting. Mathematical modelling of natural phenomena, Tome 5 (2010) no. 7 Supplement, pp. 132-138. doi : 10.1051/mmnp/20105722. http://geodesic.mathdoc.fr/articles/10.1051/mmnp/20105722/

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