Enhancing Water Network Resilience: A Generative AI Framework For Proactive Leakage And Loss Detection

Non-Revenue Water (NRW) is a major problem to urban water utilities across the globe, but physical leakage in old and complicated distribution systems is the main cause. Although sensing technologies, smart water networks, and data-based analytics have advanced, the majority of current leakage detection methods are reactive, depending on historical data, predetermined fault events, or discriminative models of machine learning with a limited capacity to predict rare or previously unknown leakage events. This review is a critical analysis of the development of leakage detection methodologies, including traditional methods of leakage detection such as physical method, artificial intelligence-based anomaly detection, and Digital Twin-based monitoring frameworks. The analysis shows that Digital Twins offer useful real-time system visibility and operational decision support, but their predictive functions are limited to the dependencies on scenarios and the lack of data. In order to overcome these constraints, this paper identifies the new role of Generative Artificial Intelligence as a game changer of proactive water network management. Adversarial and probabilistic generative models provide the capability to train the underlying distribution of multivariate time-series data, and to generate realistic and physically plausible leakage and anomaly scenarios. This feature can be used to augment data, train an anomaly detector, stress-test network behavior, and detect subtle or new leak signatures when incorporated into a physics-aware Digital Twin environment. The review summarizes the latest advances in the field of generative time-series modeling, anomaly detection, and Digital Twin integration, and comments on their applicability in industry, implementation issues, and considerations to implement them on a utility-scale basis. The main gaps in the research are defined, such as physics -informed generative models, explainable AI to gain the trust of operators, and field validation on a large scale. In general, the paper finds Generative AI-Enhanced Digital Twins as the prospect of predictive maintenance, enhanced network resilience, and sustainable NRW reduction in the future smart water system.