Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Oyebode, Oluwaseun, Babatunde, Damilola E, Monyei, Chukwuka G and Babatunde, Olubayo M (2019) Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals. Heliyon, 5 (11). e02796. ISSN 2405-8440

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Abstract

The purpose of this review is to establish and classify the diverse ways in which evolutionary computation (EC) techniques have been employed in water demand modelling and to identify important research challenges and future directions. This review also investigates the potentials of conventional EC techniques in influencing water demand management policies beyond an advisory role while recommending strategies for their use by policy-makers with the sustainable development goals (SDGs) in perspective. This review ultimately proposes a novel integrated water demand and management modelling framework (IWDMMF) that enables water policy-makers to assess the wider impact of water demand management decisions through the principles of egalitarianism, utilitarianism, libertarianism and sufficientarianism. This is necessary to ensure that water policy decisions incorporate equity and justice. Environmental science; Applied computing; Computing methodology; Civil engineering; Process modeling; Hydrology; evolutionary computation; water justice; water demand; Artificial intelligence; water equity; Sustainable development goals

Item Type: Article
Keywords: Applied computing, Artificial intelligence, Civil engineering, Computing methodology, Environmental science, Evolutionary computation, Hydrology, Process modelling, Sustainable development goals, Water demand, Water equity, Water justice
Schools and Departments: University of Sussex Business School > SPRU - Science Policy Research Unit
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 05 May 2022 09:08
Last Modified: 05 May 2022 09:15
URI: http://sro.sussex.ac.uk/id/eprint/105714

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