Title: Ai Smart Water Leak Detection
Authors: Rehema Malenda, Christina Longo, Happyness Paul, Sharom Mwakisopile, Shakila Sadi, Shukuru Thobias, Simon James, Abdallah Ali.
Volume: 10
Issue: 2
Pages: 31-36
Publication Date: 2026/02/28
Abstract:
Water loss in distribution systems, commonly referred to as non-revenue water (NRW), represents a major global challenge for water utilities, contributing to resource wastage, economic losses, and sustainability issues. This review paper critically examines the integration of Artificial Intelligence (AI) and related digital technologies for smart water loss detection, with a particular emphasis on addressing the constraints of aging infrastructure, limited resources, and variable operational conditions in developing and urbanizing regions. Through a systematic synthesis of recent advancements in machine learning models, sensor-based monitoring, acoustic analysis, pressure data analytics, and ensemble approaches, the analysis identifies persistent gaps in data scarcity, model generalization, noise handling, real-time deployment, and integration with existing systems. While individual AI techniques demonstrate high detection accuracy in controlled settings, their practical application remains fragmented, often reliant on high-quality labeled data or cloud infrastructure that may not suit intermittent connectivity or low-resource environments. In response, this paper proposes a conceptual framework for a unified "AI Smart Water Loss Detection" platform that synergistically combines anomaly detection algorithms, edge computing for on-device processing, IoT sensor fusion, and adaptive learning within a resilient, hybrid architecture. The paper concludes with strategic recommendations for researchers, utilities, and policymakers to advance effective, scalable, and equitable AI-driven solutions that minimize water losses, enhance infrastructure resilience, and support sustainable water management