Development of river water quality management using fuzzy techniques: a review

Fuzzy theory is a powerful tool with the capability of solving many complex problems which include river water quality assessment when dealing with uncertainties data and vagueness that occur in a river system. In this paper, various fuzzy techniques are applied in the development of river water qua...

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Bibliographic Details
Published in:International Journal of River Basin Management
Main Author: Che Osmi S.F.; Malek M.A.; Yusoff M.; Azman N.H.; Faizal W.M.
Format: Review
Language:English
Published: Taylor and Francis Ltd. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959047944&doi=10.1080%2f15715124.2015.1105232&partnerID=40&md5=2942d3757c04e7e8cc52692b0aedfb82
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Summary:Fuzzy theory is a powerful tool with the capability of solving many complex problems which include river water quality assessment when dealing with uncertainties data and vagueness that occur in a river system. In this paper, various fuzzy techniques are applied in the development of river water quality management; particularly, water quality index assessment is outlined. Uncertainties involved in river water quality management, especially from random nature of hydrologic variables and missing data, can be overcome using fuzzy techniques. Fuzzy interference system (FIS) plays an important role in fuzzy excursion. FIS can be improved using other methods such as similarity measures and grey clustering method to obtain better and accurate water quality assessment results. FIS can also be integrated with an expert system and a decision support system to assist the decision-maker in improving river water quality through effective strategies. Thus, the capability of FIS to be incorporated with other techniques such as grey clustering, artificial neural network, and expert system to provide a comprehensive solution in order to control river pollution is demonstrated. © 2015 International Association for Hydro-Environment Engineering and Research.
ISSN:15715124
DOI:10.1080/15715124.2015.1105232