Digital Twin-Driven Predictive Analytics for Smart Water Distribution Networks Using Internet of Things

Abstract

Urban water distribution systems face significant challenges, including leakage, pressure fluctuations, aging infrastructure, and inefficient resource utilization. This paper introduces a digital twin-driven predictive analytics framework that integrates Internet of Things (IoT) sensors, edge computing, and deep learning algorithms for intelligent water distribution management. Real-time hydraulic parameters are continuously synchronized with a virtual digital twin model to predict leakage events and optimize water distribution strategies. Long Short-Term Memory (LSTM) networks forecast demand patterns, while anomaly detection algorithms identify abnormal pressure variations indicative of pipe failures. Experimental evaluation demonstrates improved leakage detection accuracy, reduced water loss, and enhanced operational efficiency compared to conventional monitoring techniques. The proposed framework supports sustainable smart city infrastructure through intelligent water resource management.