Math. Model. Nat. Phenom.
Volume 5, Number 7, 2010JANO9 – The 9th International Conference on Numerical Analysis and Optimization
|Page(s)||132 - 138|
|Published online||26 August 2010|
Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
Department of Civil Engineering, Mohammadia School of Engineering
University Mohammed V-Agdal, Rabat, Morocco
* Corresponding author: E-mail:
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
Mathematics Subject Classification: 78M32
Key words: artificial neural network / particle swarm optimization algorithm / back propagation / daily streamflows / catchment / semi-arid climate
© EDP Sciences, 2010
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