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http://hdl.handle.net/10397/1297
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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