Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1195
Title: A split-step particle swarm optimization algorithm in river stage forecasting
Authors: Chau, KW 
Keywords: River stage forecasting
Split-step
Particle swarm optimization
Levenberg-Marquardt algorithm
Artificial neural networks
Issue Date: 30-Nov-2007
Publisher: Elsevier
Source: Journal of hydrology, 30 Nov. 2007, v. 346, no. 3-4, p. 131-135 How to cite?
Journal: Journal of hydrology 
Abstract: An accurate forecast of river stage is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures as required. Since a variety of existing process-based hydrological models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. In this paper, a split-step particle swarm optimization (PSO) model is developed and applied to train multi-layer perceptrons for forecasting real-time water levels at Fo Tan in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging station (Tin Sum) or at Fo Tan. This paradigm is able to combine the advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg–Marquardt algorithm in the second step. The results demonstrate that it is able to attain a higher accuracy in a much shorter time when compared with the benchmarking backward propagation algorithm as well as the standard PSO algorithm.
URI: http://hdl.handle.net/10397/1195
ISSN: 0022-1694
DOI: 10.1016/j.jhydrol.2007.09.004
Rights: Journal of Hydrology © 2007 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com.
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