Integrating Digital Twins with Machine Learning for Intelligent Monitoring of Water Distribution Systems

As urban populations grow and aging infrastructure undergoes increasing strain, the need to keep Water Distribution Systems (WDS) as efficient, resilient, and sustainable as possible has become a priority for modern cities. In order to do so, this paper presents a holistic framework that blends Digital Twin (DT) with Machine Learning (ML) for intelligent monitoring and controlling of Water Distribution Systems (WDS). This system is based on the construction of an online virtual model of a water network using EPANET simulations, and additionally, it carries out anomaly detection, demand forecasting, leak identification, and pressure control through the implementation of machine learning algorithms. The datasets include real sensor data (pressure, flow rate, turbidity, pH), SCADA logs, and synthetic anomalies generated from simulations. An Isolation Forest model achieved 97.8% accuracy for identifying system anomalies, while a Long Short-Term Memory (LSTM) model returned an $R^{2}$ of $\mathbf{9 4 \%}$ in forecasting water demand. As a result, a Deep Q-Learning agent reduced zone pressure variance by 66.3 %, thus enabling performance improvements and a reduction in energy consumption. This end-to-end use of cloud and edge technologies builds an integrated DT-ML pipeline that utilizes real-time data streaming, analytics, and control actions deployed through a feedback loop. The results are visualized via Grafana dashboards to assist utility operators with intuitive monitoring. The study demonstrates the transformative potential of combining digital technologies and machine learning to shift from reactive monitoring to proactive and autonomous water management. The proposed approach ensures both service availability and optimal resource utilization, and lays the foundation for future scalability of autonomous infrastructure systems in alignment with Industry 4.0 goals.