Abstract
AI and machine learning techniques have already demonstrated significant outcomes in various water industry applications such as water quality monitoring, chemical dosing, prioritising active leakage detection areas, intelligent network optimisation, and the prediction of water pipe failure. Can these techniques be extended from water utility operations (Anda, 2017) into home and commercial water usage (Schmack et al., 2019)? The introduction of a reward credit system to those residents who actively save energy-intensive mains water and wastewater, whilst optimally managing aquifer recharge, can support localised, hybrid water sources at residential and community scale (Fornarelli et al., 2019). While currently, machine-learning algorithms are being used to detect inaccuracies or anomalies in water meter data, in the future, AI and machine learning techniques can be used to better manage the use of alternate water sources in cities to achieve sustainable hybrid water systems (Fornarelli et al., 2021).