Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1275
Title: Rainfall-runoff correlation with particle swarm optimization algorithm
Authors: Chau, KW 
Keywords: Particle swarm optimization
Artificial neural networks
Siu Lek Yuen
Issue Date: 2004
Publisher: Springer-Verlag
Source: In F Yin, J Wang & C Guo (Eds.), Advances in neural networks--ISNN 2004 : International Symposium on Neural Networks, Dalian, China, August 19-21, 2004 : proceedings, p. 970-975. Berlin: Springer-Verlag, 2004 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 3174
Abstract: A reliable correlation between rainfall-runoff enables the local authority to gain more amble time for formulation of appropriate decision making, issuance of an advanced flood forewarning, and execution of earlier evacuation measures. Since a variety of existing methods such as rainfall-runoff modeling or statistical techniques involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution, provided that their drawbacks can be overcome. Usual problems in the training with gradient algorithms are the slow convergence and easy entrapment in a local minimum. This paper presents a particle swarm optimization model for training perceptrons. It is applied to forecasting real-time runoffs in Siu Lek Yuen of Hong Kong with different lead times on the basis of the upstream gauging stations or at the specific station. It is demonstrated that the results are both more accurate and faster to attain, when compared with the benchmark backward propagation algorithm.
URI: http://hdl.handle.net/10397/1275
ISBN: 978-3-540-22843-1
DOI: 10.1007/b99834
Rights: © Springer-Verlag Berlin Heidelberg 2004. The original publication is available at http://www.springerlink.com.
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