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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1271

Title: River stage forecasting with particle swarm optimization
Authors: Chau, Kwok-wing
Subjects: Algorithms
Neural networks
Statistical methods
Issue Date: 2004
Publisher: Springer-Verlag Berlin Heidelberg
Citation: Lecture notes in artificial intelligence, 2004, v. 3029, p.1166-1173.
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 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 easily getting stuck in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting real-time 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 from the verification simulations that faster and more accurate results can be acquired.
Description: DOI: 10.1007/b97304
Rights: © Springer-Verlag Berlin Heidelberg 2004. The original publication is available at http://www.springerlink.com.
Type: Book/Book Chapter
URI: http://hdl.handle.net/10397/1271
ISBN: 978-3-540-22007-7
Appears in Collections:CEE Book Chapters

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