Abstract
Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.
Highlights
In a recent report published by the World Economic Forum, water scarcity was identified as one of the largest global risks because only 0.014% of all water is fresh and accessible [1].Four factors can be contribute to water scarcity: (1) uneven geographic distribution of water sources;(2) urbanization with rapid growth in population and economy; (3) poor water resource management; and (4) prolonged drought [2,3]
We acknowledge the findings from this study, we argue that the incorporation of near real-time feedback along with personalised recommendations would have improved the effectiveness of the programs
The aim of this section is to highlight the findings that emerged from the critical analysis of the literature pertaining to the five themes identified for this study
Summary
(2) urbanization with rapid growth in population and economy; (3) poor water resource management; and (4) prolonged drought [2,3] For these reasons, many metropolitan water utilities are facing challenges, such as ensuring water supply during water shortages caused by prolonged drought and avoiding low water pressure during the hours of peak demand [4]. Many metropolitan water utilities are facing challenges, such as ensuring water supply during water shortages caused by prolonged drought and avoiding low water pressure during the hours of peak demand [4] These challenges have paved the way for a smart technology-based, updated water distribution infrastructure that supports safe, reliable, and sustainable water supply to consumers [5], including by supporting water demand management. WDM has five categories: (1) engineering (i.e., upgrading to more water-efficient appliances);
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