A state-of-the-art review of scientific, industrial, and commercial developments in water leakage management for water supply systems.

Detecting water leakage represents a significant and pervasive challenge in water supply systems due to the complexity of network behavior, and the diversity of leakage manifestations, which may remain undetected. The magnitude of leakage varies considerably, from 3% to over 50%, depending on the level of maintenance undertaken. The global volume of water loss per year is estimated to be 126 billion cubic meters, equivalent to a financial loss of over 30 billion USD. To address this problem, leakage management strategies have been developed, including preventive measures, systematic assessment protocols, regulatory frameworks, stringent control measures, detection and localization techniques, and prompt repair initiatives. The detection and localization phase is particularly important due to its inherent complexity and critical role, where various techniques are used, including hardware- and software-based, and hybrid methods. These techniques include the implementation of Machine Learning models that provide an advantage in automated data analysis, leading to faster detection and localization of leakage in water supply systems. Moreover, commercial solutions and patented technologies are continuing to converge into more integrated and scalable monitoring strategies. This paper presents a state-of-the-art review of scientific, commercial, and industrial developments in water leakage management for water supply systems. The scientific review provides a critical and integrative comparison of existing methodologies, highlighting the trade-offs regarding practical applicability, including scalability, data dependency, uncertainty propagation, and localization accuracy. Additionally, Machine Learning applications to this field, as well as leak detection and localization performance indicators, are presented. A focused analysis of the BattLeDIM competition highlights leading methodologies and underlines the potential of hybrid approaches that merge hydraulic modeling with advanced analytics. Furthermore, the commercial and industrial analysis provides valuable insights into the main technologies and techniques employed by leading companies, and the current status of registered patents regarding water leakage detection and localization, highlighting interesting trends. Overall, this review bridges scientific, commercial, and industrial knowledge across sectors and highlights key priorities for advancing scalable and operationally robust leakage detection strategies.