TwinAI: A digital twin and graph reinforcement learning framework for real-time management of water distribution networks

Efficient management of water distribution networks is increasingly critical as aging infrastructure, limited sensing coverage and widespread leakages drive significant losses of treated water. Existing monitoring systems rely on sparse IoT deployments, and current analytical approaches for detection, localiza- tion and mitigation remain fragmented, with limited integration between hydraulic models, data-driven methods and real-time decision making. To address these limitations, this work proposes TwinAI, an integrated frame- work that couples a digital twin of the network with an autonomous graph reinforcement learning agent for real-time leakage management. The digital twin is implemented through Dyn-WNTR, an extension of the EPANET- based Water Network Tool for Resilience (WNTR) simulator that enables interactive, physically consistent hydraulic simulations. The agent observes network conditions, processes them through graph-based representations and performs control actions such as leak isolation and flow reconfiguration. This combination creates a unified environment for real-time anomaly response, continuous decision making and what-if analysis. Our proposed framework lays the groundwork for future intelligent water network management systems capable of operating robustly with sparse sensing and evolving conditions.