Leak Learning for Graph-based State Interpolation in Water Distribution Networks*

Graph-based state interpolation (GSI) is a state-of-the-art state reconstruction technique that operates over water distribution networks (WDN). This method retrieves the complete hydraulic state (nodal heads) of the network based on its topology and limited pressure measurements. To perform leak localization, GSI is coupled with a process that compares interpolated leak and leak-free states. This article presents a methodology to adapt GSI in order to learn from its off-line and online operation (i.e., gain knowledge about historical located leaks, as well as leaks appearing in the network) and using this information to improve localization in future leak events. The methodology is tested over a well-known case study (Modena), showing promising results in terms of localization performance.