Gradient Boosting Trees for Fault Identification in Water Distribution Networks

Events such as pipe burst, pipe clogging, excess demand often disrupt the water distribution network operation. As a result, water utilities have to bear an extra financial burden. The knowledge of such events could help the water supply board formulate and operate the network with minimal loss. This work aims to identify the nature of such uncertain events with limited operational information using simple measures designed to monitor the network locally at nodes. Instead of acquiring information from all the nodes, a few nodes are identified for sensor placement by solving a Mixed Integer Linear Programming (MILP) problem. The objectives for solving MILP are to minimise total sensor cost and maximise the sensitivity of measurements to any given fault. Sensitivity is calculated from operational data generated by performing Monte Carlo simulations. These simulations generate data for each fault of different magnitude at different locations. Gradient boosting trees are trained using the limited operational information from the network. Accuracy of identification of faults of 78% on the passive network and over 81% on active networks was obtained using limited features with extreme gradient boosting (XGBoost) model.