A review on integration of artificial intelligence into water quality modelling

Research output: Journal article publicationJournal articleAcademic researchpeer-review

125 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)726-733
Number of pages8
JournalMarine Pollution Bulletin
Volume52
Issue number7
DOIs
Publication statusPublished - 31 Jul 2006

Keywords

  • Artificial intelligence
  • Artificial neural network
  • Fuzzy inference system
  • Genetic algorithm
  • Knowledge-based system
  • Water quality modelling

ASJC Scopus subject areas

  • Aquatic Science
  • Oceanography
  • Pollution

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