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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1297

Title: Algal bloom prediction with particle swarm optimization algorithm
Authors: Chau, Kwok-wing
Subjects: Algal blooms
Algorithms
Particle swarm optimization
Artificial neural networks
Fisheries
Water quality
Cost effectiveness
Benchmarking
Tolo Harbour
Issue Date: 2005
Publisher: Springer Berlin / Heidelberg
Citation: Lecture notes in artificial intelligence, 2005, v. 3801, p. 645-650.
Abstract: Precise prediction of algal booms is beneficial to fisheries and environmental management since it enables the fish farmers to gain more ample time to take appropriate precautionary measures. 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. However, in order to accomplish this goal successfully, usual problems and drawbacks in the training with gradient algorithms, i.e., slow convergence and easy entrapment in a local minimum, should be overcome first. This paper presents the application of a particle swarm optimization model for training perceptrons to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong, with different lead times on the basis of several input hydrodynamic and/or water quality variables. It is shown that, when compared with the benchmark backward propagation algorithm, its results can be attained both more accurately and speedily.
Description: DOI: 10.1007/11596448_95
Rights: © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.
Type: Book/Book Chapter
URI: http://hdl.handle.net/10397/1297
ISBN: 978-3-540-30818-8
Appears in Collections:CEE Book Chapters

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