Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1297
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Title: Algal bloom prediction with particle swarm optimization algorithm
Authors: Chau, KW 
Issue Date: 2005
Source: In Y Hao, J Liu, Y Wang, YM Cheung, H Yin, L Jiao, J Ma & YC Jiao (Eds.), Computational intelligence and security : International Conference, CIS 2005, Xi'an, China, December 15-19, 2005 : proceedings, p. 645-650. Berlin ; New York: Springer, 2005
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.
Keywords: Algal blooms
Algorithms
Particle swarm optimization
Artificial neural networks
Fisheries
Water quality
Cost effectiveness
Benchmarking
Tolo Harbour
Publisher: Springer
ISBN: 978-3-540-30818-8
DOI: 10.1007/11596448_95
Rights: © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.
Appears in Collections:Book Chapter

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