# Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting

Mathematical Modelling of Natural Phenomena (2010)

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

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topSedki, 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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