PolyU IR

PolyU Institutional Repository >
Civil and Environmental Engineering >
CEE Journal/Magazine Articles >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1194

Title: Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River
Authors: Chau, Kwok-wing
Subjects: Particle swarm optimization
Artificial neural networks
Shing Mun River
Issue Date: 15-Oct-2006
Publisher: Elsevier
Citation: Journal of hydrology, 15 Oct. 2006, v. 329, no. 3-4, p. 363-367.
Abstract: An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks (ANN) has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks such as very slow convergence and easy entrapment in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is applied to predict water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown that the PSO technique can act as an alternative training algorithm for ANNs.
Description: DOI: 10.1016/j.jhydrol.2006.02.025
Rights: Journal of Hydrology © 2006 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1194
ISSN: 0022-1694
Appears in Collections:CEE Journal/Magazine Articles

Files in This Item:

File Description SizeFormat
JH3.pdfPre-published version87.42 kBAdobe PDFView/Open
Locate publisher version via PolyU eLinks

Facebook Facebook del.icio.us del.icio.us LinkedIn LinkedIn

All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
No item in the PolyU IR may be reproduced for commercial or resale purposes.


© Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Powered by DSpace (Version 1.5.2)  © MIT and HP
Feedback | Privacy Policy Statement | Copyright & Restrictions - Feedback