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.

The aim of residential water end-use studies is to disaggregate water consumption into different water end-use categories (i.e., shower, toilet, etc.). The authors previously developed a beta application software (i.e., Autoflow v2.1) that provides an intelligent platform to autonomously categorize residential water consumption data and generate management analysis reports. However, the Autoflow v2.1 software water end use event recognition accuracy achieved was between 75 to 90%, which leaves room for improvement. In the present study, a new module augmented to the existing procedure improved flow disaggregation accuracy, which resulted in Autoflow v3.1. The new module applied self-organizing maps (SOM) and K-means clustering algorithms for undertaking an initial pre-grouping of water end-use events before the existing pattern recognition procedures were applied (i.e., ANN, HMM, etc.) For validation, a dataset consisting of over 100,000 events from 252 homes in Australia were employed to verify accuracy improvements derived from augmenting the new hybrid SOM and K-means algorithm techniques into the existing Autoflow v2.1 software. The water end use event categorization accuracy ranged from 86 to 94.2% for the enhanced model (Autoflow v3.1), which was a 1.7 to 9% improvement on event categorization.

Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand/model potential second waves of contagions.

Transforming smart meter data captured at low-resolution litre intervals of 15 to 60 mins into residential water end use data provides valuable insights for water businesses and their customers. Water end use data is crucial for developing effective and customised water conservation and management strategies. In this study, a significant water end use event data repository collated from studies covering various Australian metropolitan cities was used to develop an intelligent model for low-resolution water consumption data based on Volume and Time features. The model architecture integrates the strengths of Random Forest, Linear Regression, and Neural Networks in a stacked ensemble, with a Regression Tree as a meta-model. Model accuracy was found to stabilise with of sufficient size and observation period. The model was then applied to a larger dataset, enabling a robust case study. The results demonstrate that the model accurately predicts end use categories, offering valuable insights into large-scale water consumption behaviours. This research underscores the importance of feature selection and optimal dataset size in enhancing model accuracy. The derived residential water end use model makes a significant contribution by allowing water businesses to autonomously and accurately characterise citywide residential end uses using only mainstream low-resolution smart meter technology.

Water utilities are increasingly turning to digital solutions to promote conservation behaviours among households; however, traditional campaigns often suffer from limited personalisation, low interactivity, and modest long-term impact. Though computer-tailored and recommender systems (RSs) may offer personalisation, these systems lack a generalised framework that integrates behavioural theory with system design. This study addresses this research gap by introducing a novel framework that unites behavioural science, user experience (UX) design, and adaptive digital feedback to foster water-conscious practices at the residential level. The model draws on established behavioural theories, including the Theory of Planned Behaviour, the Transtheoretical Model, and Intervention Mapping, to ensure that tailored recommendations align with users’ psychological drivers, behavioural readiness, and daily routines. An industry-first prototype RS was developed and evaluated through an online survey (N = 300), assessing user perceptions of relevance, motivation, ease of use, and likelihood of action. The results reveal strong support for personalised suggestions, with 82% of respondents agreeing that personalised recommendations would help conserve water, and 76% indicating incentives would motivate adoption. This evidence indicates early acceptance and high potential impact. This study also addresses a critical research gap: no generic model previously existed to guide the integration of RSs with behaviour change interventions in water demand management. Broader implications are also discussed for applying the model to other sustainability domains such as energy use, waste reduction, and climate adaptation.