Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1224
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Chau, KW | - |
dc.date.accessioned | 2014-12-11T08:27:28Z | - |
dc.date.available | 2014-12-11T08:27:28Z | - |
dc.identifier.issn | 0025-326X | - |
dc.identifier.uri | http://hdl.handle.net/10397/1224 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | Marine Pollution Bulletin © 2006 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com. | en_US |
dc.subject | Water quality modelling | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Knowledge-based system | en_US |
dc.subject | Genetic algorithm | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Fuzzy inference system | en_US |
dc.title | A review on integration of artificial intelligence into water quality modelling | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 726 | - |
dc.identifier.epage | 733 | - |
dc.identifier.volume | 52 | - |
dc.identifier.issue | 7 | - |
dc.identifier.doi | 10.1016/j.marpolbul.2006.04.003 | - |
dcterms.abstract | With 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Marine pollution bulletin, July 2006, v. 52, no. 7, p. 726-733 | - |
dcterms.isPartOf | Marine pollution bulletin | - |
dcterms.issued | 2006-07 | - |
dc.identifier.isi | WOS:000239854400010 | - |
dc.identifier.scopus | 2-s2.0-33746329318 | - |
dc.identifier.pmid | 16764895 | - |
dc.identifier.rosgroupid | r25863 | - |
dc.description.ros | 2005-2006 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
MPB4.pdf | Pre-published version | 81.89 kB | Adobe PDF | View/Open |
Page views
236
Last Week
0
0
Last month
Citations as of Mar 24, 2024
Downloads
1,454
Citations as of Mar 24, 2024
SCOPUSTM
Citations
188
Last Week
0
0
Last month
2
2
Citations as of Mar 28, 2024
WEB OF SCIENCETM
Citations
161
Last Week
0
0
Last month
1
1
Citations as of Mar 28, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.