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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1191

Title: A review on the integration of artificial intelligence into coastal modeling
Authors: Chau, Kwok-wing
Subjects: Artificial intelligence
Coastal modeling
Knowledge-based systems
Issue Date: Jul-2006
Publisher: Elsevier
Citation: Journal of environmental management, Jul. 2006, v. 80, no. 1, p. 47-57.
Abstract: With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.
Description: DOI: 10.1016/j.jenvman.2005.08.012
Rights: Journal of Environmental Management © 2005 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1191
ISSN: 0301-4797
Appears in Collections:CEE Journal/Magazine Articles

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