AI-Enabled Smart Water Distribution Systems with Predictive Leak Detection

Abstract

Rapid urbanization, industrialization, and climate change have intensified water scarcity, creating a need for intelligent water distribution systems. Traditional leak detection methods rely on manual inspections and threshold-based monitoring, resulting in delayed detection, high water losses, increased maintenance costs, and infrastructure damage. This study proposes an AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, cloud-edge computing, and predictive analytics for real-time pipeline monitoring and early leak detection. The framework collects data from pressure, flow, acoustic, and water quality sensors, applying machine learning algorithms to identify hydraulic anomalies and predict leakage probabilities. It also incorporates digital twins and hydraulic simulation models to improve adaptability under varying operational conditions. Mathematical models evaluate leak probability, sensor reliability, and system performance, enabling proactive maintenance and informed decision-making. The proposed architecture enhances detection accuracy, minimizes false alarms, reduces non-revenue water losses and operational costs, and improves infrastructure resilience, supporting sustainable, reliable, and intelligent water resource management.