Interpretable Event Diagnosis in Water Distribution Networks

The increasing penetration of information and communication technologies in the design, monitoring, and control of water systems enables the use of algorithms for detecting and identifying unanticipated events (such as leakages or water contamination) using sensor measurements. However, data-driven methodologies do not always give accurate results and are often not trusted by operators, who may prefer to use their engineering judgment and experience to deal with such events. In this work, we propose a framework for interpretable event diagnosis -- an approach that assists the operators in associating the results of algorithmic event diagnosis methodologies with their own intuition and experience. This is achieved by providing contrasting (i.e., counterfactual) explanations of the results provided by fault diagnosis algorithms; their aim is to improve the understanding of the algorithm's inner workings by the operators, thus enabling them to take a more informed decision by combining the results with their personal experiences. Specifically, we propose counterfactual event fingerprints, a representation of the difference between the current event diagnosis and the closest alternative explanation, which can be presented in a graphical way. The proposed methodology is applied and evaluated on a realistic use case using the L-Town benchmark. Introduction When an event, such as a leakage, occurs in a Water Distribution Network (WDN), this can affect the dynamics of the system by causing changes in the pressures and flows [1]. These changes can be monitored by flow and pressure sensors installed within WDNs. Typically, a limited number of flow sensors are installed at the entrance of District Metered Areas (DMAs) to monitor the overall water inflow in the area [2], while a larger number of pressure sensors (due to reduced capital and installation costs) are installed at certain locations within the DMA to improve leakage detectability [3].