Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1224
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorChau, KW-
dc.date.accessioned2014-12-11T08:27:28Z-
dc.date.available2014-12-11T08:27:28Z-
dc.identifier.issn0025-326X-
dc.identifier.urihttp://hdl.handle.net/10397/1224-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rightsMarine Pollution Bulletin © 2006 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.en_US
dc.subjectWater quality modellingen_US
dc.subjectArtificial intelligenceen_US
dc.subjectKnowledge-based systemen_US
dc.subjectGenetic algorithmen_US
dc.subjectArtificial neural networken_US
dc.subjectFuzzy inference systemen_US
dc.titleA review on integration of artificial intelligence into water quality modellingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage726-
dc.identifier.epage733-
dc.identifier.volume52-
dc.identifier.issue7-
dc.identifier.doi10.1016/j.marpolbul.2006.04.003-
dcterms.abstractWith the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationMarine pollution bulletin, July 2006, v. 52, no. 7, p. 726-733-
dcterms.isPartOfMarine pollution bulletin-
dcterms.issued2006-07-
dc.identifier.isiWOS:000239854400010-
dc.identifier.scopus2-s2.0-33746329318-
dc.identifier.pmid16764895-
dc.identifier.rosgroupidr25863-
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
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