A proof-of-concept study towards developing digital twins for operational excellence in large-scale water distribution networks

In the current face of water scarcity, water losses due to leakages in large-scale water distribution networks (WDN) and non-revenue water are challenging factors. In this direction, Digital Twins integrate concepts like IoT, ML (machine learning), and DL (deep learning) with a water pave path for smart urban water infrastructure. Herein, we propose a holistic digital twin systems framework and its application in leak detection, validated with field-data on Indian Institute of Technology Jodhpur (IIT-J) campus WDN. A detailed methodology developing monitoring digital twins supported on the python platform and using open-source WDN simulators; EPANET and WNTR for hydraulic simulations and a Graph-convolution Neural Network-based leak detection model is elucidated. Results are analysed and demonstrated for the highest water consumption zone of the campus, with the model giving an accuracy of 90% for leakage detection. Further, a test scenario is described where the proposed framework shows water savings of up to 58% which would have been otherwise lost due to leaks in WDN.