Digital Twin Technology for Smart Water Distribution and Wastewater Management: Current Status and Future Perspectives

Abstract

Digital Twin (DT) technology has emerged as a transformative approach for the digitalization of water distribution net- works and wastewater management systems. By integrating real-time sensor data, Internet of Things (IoT), artificial intel- ligence (AI), machine learning (ML), cloud computing, hydraulic modeling, and predictive analytics, digital twins enable continuous monitoring, simulation, optimization, and decision support. DTs improve leak detection, predictive mainte- nance, water quality monitoring, energy efficiency, and resilience against climate change and infrastructure failures. This review critically examines recent advances in digital twin technology for smart water distribution and wastewater treat- ment, covering system architectures, enabling technologies, applications, benefits, limitations, and future research direc- tions. Emerging trends including explainable AI, federated learning, edge computing, generative AI, cyber-physical sys- tems, and autonomous water management are also discussed. Finally, major research gaps are identified to guide future investigations toward intelligent and sustainable water infrastructure.