Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1277
Title: A split-step PSO algorithm in prediction of water quality pollution
Authors: Chau, KW 
Keywords: Particle swarm optimization
Artificial neural networks
Algorithms
Backpropagation
Benchmarking
Cost effectiveness
Algae
Tolo Harbour
Issue Date: 2005
Publisher: Springer
Source: In J Wang, X Liao & Z Yi (Eds.), Advances in neural networks--ISNN 2005 : Second International Symposium on Neural Networks, Chongqing, China, May 30-June 1, 2005 : proceedings, p. 1034-1039. Berlin: Springer, 2005 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 3498
Abstract: In order to allow the key stakeholders to have more float time to take appropriate precautionary and preventive measures, an accurate prediction of water quality pollution is very significant. Since a variety of existing water quality models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong. 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 are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the usual PSO algorithm, it attains a higher accuracy in a much shorter time.
URI: http://hdl.handle.net/10397/1277
ISBN: 978-3-540-25914-5
DOI: 10.1007/11427469_164
Rights: © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.
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