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      Multi-Criteria Decision-Making Methods to Address Water Allocation Problems: A Systematic Review

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      Water
      MDPI AG

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          Abstract

          The water allocation problem is complex and requires a combination of regulations, policies, and mechanisms to support water management to minimize the risk of shortage among competing users. This paper compiles the application of multi-criteria decision-making (MCDM) related to water allocation. In this regard, this paper aims to identify and to discern the pattern, distribution of study regions, water problem classifications, and decision techniques application for a specific water allocation problem. We applied a systematic literature review study from 2000 to 2019 by using four literature databases (Web of Science, Scopus, Science Direct, and Google Scholar). From 109 papers, 49 publications have been identified and information extracted. This study reveals that in the past two decades the application of MCDM in the area of water allocation has increased particularly after 2014. Around 65% and 12% of study papers were conducted in Asia and Europe, respectively. Water shortage, water use management, and water quality were consecutively the most top-ranked discussed water problems. NSGA II (non-dominated sorting genetic algorithm), GA (genetic algorithm), and LP (linear programming) are the more often applied decision methods to solve water allocation problems. The key findings of this study provide guidelines for future research studies.

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          A fast and elitist multiobjective genetic algorithm: NSGA-II

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            Economic Growth and the Environment

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              Four billion people facing severe water scarcity

              Global water scarcity assessment at a high spatial and temporal resolution, accounting for environmental flow requirements.
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                Author and article information

                Contributors
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                Journal
                WATEGH
                Water
                Water
                MDPI AG
                2073-4441
                January 2021
                January 07 2021
                : 13
                : 2
                : 125
                Article
                10.3390/w13020125
                4a9270e0-f3cc-48ed-b051-c53efde98069
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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