AI-Enabled Smart Water Distribution Systems with Predictive Leak Detection

Abstract

Rapid urbanization, industrialization, and climate change have intensified water scarcity, increasing the need for intelligent water distribution systems. Traditional leak detection methods are often slow, labor-intensive, and unable to identify leaks at an early stage, resulting in water loss, higher operational costs, and infrastructure damage. This study proposes an AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, edge-cloud computing, and predictive analytics for real-time leak detection and proactive maintenance. The framework combines intelligent sensing, machine learning, hydraulic analysis, and digital twin technologies to identify pressure, flow, and acoustic anomalies associated with pipeline degradation. Mathematical models are incorporated to estimate leak probability, evaluate sensor reliability, and assess system performance. The proposed architecture enhances detection accuracy, reduces false alarms and non-revenue water (NRW) losses, lowers maintenance costs, and improves infrastructure resilience. Overall, the framework provides a scalable and sustainable solution for intelligent water resource management through continuous monitoring and AI-driven decision support.