Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting

A. Sedki; D. Ouazar

Mathematical Modelling of Natural Phenomena (2010)

  • Volume: 5, Issue: 7, page 132-138
  • ISSN: 0973-5348

Abstract

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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

How to cite

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Sedki, A., and Ouazar, D.. Taik, A., ed. "Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting." Mathematical Modelling of Natural Phenomena 5.7 (2010): 132-138. <http://eudml.org/doc/197680>.

@article{Sedki2010,
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},
author = {Sedki, A., Ouazar, D.},
editor = {Taik, A.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {artificial neural network; particle swarm optimization algorithm; back propagation; daily streamflows; catchment; semi-arid climate},
language = {eng},
month = {8},
number = {7},
pages = {132-138},
publisher = {EDP Sciences},
title = {Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting},
url = {http://eudml.org/doc/197680},
volume = {5},
year = {2010},
}

TY - JOUR
AU - Sedki, A.
AU - Ouazar, D.
AU - Taik, A.
TI - Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
JO - Mathematical Modelling of Natural Phenomena
DA - 2010/8//
PB - EDP Sciences
VL - 5
IS - 7
SP - 132
EP - 138
AB - 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
LA - eng
KW - artificial neural network; particle swarm optimization algorithm; back propagation; daily streamflows; catchment; semi-arid climate
UR - http://eudml.org/doc/197680
ER -

References

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  7. A. Salman, I. Ahmad, S. Al-Madani. Particle swarm optimization for task assignment problem. Microproc. and Microsyst., 26 (2002), No. 8, 363-371. 
  8. R. S. Sexton, R. E. Dorsey, J. D. Johnson. Toward global optimization of neural networks: A comparison of the genetic algorithm and back propagation. Decision Support Systems, 22 (1998), 171–185. 
  9. J.M. Yang, C.Y. Kao. A robust evolutionary algorithm for training neural networks. Neural Comput. Appl., 10 (2001), 214–230. Zbl0989.68111

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