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
Source: 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.
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
DOI: 10.1007/11596448_95
ISBN: 978-3-540-30818-8
Appears in Collections:CEE Book Chapters

Files in This Item:
File Description SizeFormat 
LNAI13.pdfPre-published version194.45 kBAdobe PDFView/Open


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.