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

Voir la notice de l'article provenant de la source EDP Sciences

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 
@article{10_1051_mmnp_20105722,
     author = {A. Sedki and D. Ouazar},
     title = {Hybrid {Particle} {Swarm} and {Neural} {Network} {Approach} for {Streamflow} {Forecasting}},
     journal = {Mathematical modelling of natural phenomena},
     pages = {132--138},
     publisher = {mathdoc},
     volume = {5},
     number = {7 Supplement},
     year = {2010},
     doi = {10.1051/mmnp/20105722},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/mmnp/20105722/}
}
TY  - JOUR
AU  - A. Sedki
AU  - D. Ouazar
TI  - Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
JO  - Mathematical modelling of natural phenomena
PY  - 2010
SP  - 132
EP  - 138
VL  - 5
IS  - 7 Supplement
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/articles/10.1051/mmnp/20105722/
DO  - 10.1051/mmnp/20105722
LA  - en
ID  - 10_1051_mmnp_20105722
ER  - 
%0 Journal Article
%A A. Sedki
%A D. Ouazar
%T Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
%J Mathematical modelling of natural phenomena
%D 2010
%P 132-138
%V 5
%N 7 Supplement
%I mathdoc
%U http://geodesic.mathdoc.fr/articles/10.1051/mmnp/20105722/
%R 10.1051/mmnp/20105722
%G en
%F 10_1051_mmnp_20105722
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

[1] Q. Duan, S. Sorooshian, V. Gupta Effective and efficient global optimization for conceptual rainfall runoff models Water Resour. Res 1992 1015 1031

[2] R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory Proc. of 6th Symp. on micro machine and human science, IEEE service center, Piscataway, N.J., (1995), 39-43.

[3] K.L. Hsu, H.V. Gupta, S. Sorooshian Artificial neural network modeling of the rainfall-rainoff process Water Resour. Res. 1995 2517 2530

[4] V. Maniezzo Genetic evolution of the topology and weight distribution of neural networks IEEE Transaction on Neural Networks 1994 39 53

[5] K.E. Parsopoulos, M.N. Vrahatis Recent approaches to global optimization problems through particle swarm optimization Natural Comput. 2002 235 306

[6] D.E. Rumelhart, G.E. Hinton, R.J. Williams. Learning internal representation by error propagation. In: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Cambridge, MA, (1986), 318-362.

[7] A. Salman, I. Ahmad, S. Al-Madani Particle swarm optimization for task assignment problem Microproc. and Microsyst. 2002 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 1998 171 185

[9] J.M. Yang, C.Y. Kao A robust evolutionary algorithm for training neural networks Neural Comput. Appl. 2001 214 230

Cité par Sources :