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Title: A review on integration of artificial intelligence into water quality modelling
Authors: Chau, KW 
Keywords: Water quality modelling
Artificial intelligence
Knowledge-based system
Genetic algorithm
Artificial neural network
Fuzzy inference system
Issue Date: Jul-2006
Publisher: Elsevier
Source: Marine pollution bulletin, July 2006, v. 52, no. 7, p. 726-733 How to cite?
Journal: Marine pollution bulletin 
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.
ISSN: 0025-326X
DOI: 10.1016/j.marpolbul.2006.04.003
Rights: Marine Pollution Bulletin © 2006 Elsevier Ltd. The journal web site is located at
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