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Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

Asadnia, Mohsen, Chua, Lloyd H.C., Qin, X.S and Talei, Amin 2014, Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling, Journal of hydrologic engineering, vol. 19, no. 7, pp. 1320-1329, doi: 10.1061/(ASCE)HE.1943-5584.0000927.

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Title Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling
Author(s) Asadnia, Mohsen
Chua, Lloyd H.C.ORCID iD for Chua, Lloyd H.C. orcid.org/0000-0003-2523-3735
Qin, X.S
Talei, Amin
Journal name Journal of hydrologic engineering
Volume number 19
Issue number 7
Start page 1320
End page 1329
Total pages 10
Publisher American Society of Civil Engineers
Place of publication Reston, Va.
Publication date 2014-07
ISSN 1943-5584
Keyword(s) particle
particle swarm
rainfall
rainfall-runoff
modelling
Summary This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.
Language eng
DOI 10.1061/(ASCE)HE.1943-5584.0000927
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30063771

Document type: Journal Article
Collection: School of Engineering
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