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Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting

Published online by Cambridge University Press:  26 August 2010

A. Taik ,
A. Sedki  and
D. Ouazar
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Abstract

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

Keywords

artificial neural networkparticle swarm optimization algorithmback propagationdaily streamflowscatchmentsemi-arid climate
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 5 , Issue 7: JANO9 – The 9th International Conference on Numerical Analysis and Optimization , 2010 , pp. 132 - 138
Copyright
© EDP Sciences, 2010

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