Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1297
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorChau, KW-
dc.date.accessioned2014-12-11T08:27:55Z-
dc.date.available2014-12-11T08:27:55Z-
dc.identifier.isbn978-3-540-30818-8-
dc.identifier.urihttp://hdl.handle.net/10397/1297-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture notes in computer science ; v. 3801-
dc.rights© Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.en_US
dc.subjectAlgal bloomsen_US
dc.subjectAlgorithmsen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectArtificial neural networksen_US
dc.subjectFisheriesen_US
dc.subjectWater qualityen_US
dc.subjectCost effectivenessen_US
dc.subjectBenchmarkingen_US
dc.subjectTolo Harbouren_US
dc.titleAlgal bloom prediction with particle swarm optimization algorithmen_US
dc.typeBook Chapteren_US
dc.description.otherinformationAuthor name used in this publication: K. W. Chauen_US
dc.description.otherinformationSeries: Lecture notes in computer scienceen_US
dc.identifier.doi10.1007/11596448_95-
dcterms.abstractPrecise 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn 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-
dcterms.issued2005-
dc.identifier.isiWOS:000234873700095-
dc.identifier.scopus2-s2.0-33646822486-
dc.relation.ispartofbookComputational intelligence and security : International Conference, CIS 2005, Xi'an, China, December 15-19, 2005 : proceedings-
dc.relation.conferenceCIS-
dc.publisher.placeBerlin ; New Yorken_US
dc.identifier.rosgroupidr26623-
dc.description.ros2005-2006 > Academic research: refereed > Publication in refereed journal-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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