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.
Highlights
Over the past several decades, ever-growing demands for freshwater resources have increased the risks of severe water stress in many parts of the world (Fig. 1)
Since this review focuses on the application of evolutionary computation (EC) techniques for water demand forecasting, it is essential to present a brief review of popular EC techniques used in optimizing water distribution networks (WDNs) as provided
This paper has examined the extent to which EC techniques have been applied in water demand modelling and classified their application into 2 major categories namely, (i) predictive modelling, and (ii) optimization modelling
Summary
Over the past several decades, ever-growing demands for freshwater resources have increased the risks of severe water stress in many parts of the world (Fig. 1). Examples of robust soft computing techniques that have found application in water resources include, but are not limited to, artificial neural networks (ANN), fuzzy and neuro-fuzzy methods, support vector machines (SVMs), and more recently, evolutionary computation (EC) techniques These soft computing techniques and many more have been reported to have achieved varying degrees of successes in diverse water resource applications, including streamflow forecasting (Kisi and Cigizoglu, 2007; Oyebode et al, 2014a), reservoir inflow prediction (Oyebode and Adeyemo, 2014), water quality modelling (Chang et al, 2015; Dragoi et al, 2011), wastewater treatment (Enitan et al, 2014) and sediment yield modelling (Ch et al, 2013; Guven and Kisi, 2011). The aims are to (a) establish and classify the diverse ways in which EC techniques have been employed in water demand modelling; (b) identify important research challenges and future directions; (c) recommend implementation strategies for the adoption by policy-makers with water equity and justice and SDGs in perspective
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