AI-based leak detection in water distribution networks: a decadal review across sensing and monitoring scales

Abstract

Water losses in Water Distribution Networks remain a persistent challenge, with global impacts exceeding $39 billion annually. While Artificial Intelligence (AI) offers promising leak detection solutions, assessing operational readiness and reproducibility is difficult due to literature fragmented across sensing technologies and spatial scales. This systematic review of 86 studies (2015–2026) structures the field through a hierarchical hydroinformatics framework: network-level monitoring, district metered area analysis, and In-situ localization. Each level relies on distinct sensing modalities conditioning feature engineering and model selection. Crucially, the analysis reveals that the primary bottleneck preventing real-world deployment is not algorithmic sophistication, but the prevalence of isolated computational approaches that ignore physical and operational constraints. To bridge the gap between simulated validation and operational utility, this review argues for conceptualizing AI as a sensor-aware, adaptive hierarchical mechanism. Furthermore, it exposes the urgent need for a paradigm shift in evaluation standards by moving beyond traditional metrics to explicitly incorporate operational False Alarm Rates, computational feasibility, and integration with operational systems (SCADA/GIS). By aligning algorithmic evaluation with infrastructural realities, this critical assessment establishes how AI-based approaches must evolve to become a viable tool for water loss reduction.