Interpretable Event Diagnosis in Water Distribution Networks

TL;DR

This paper introduces an interpretable framework for water distribution event diagnosis that uses counterfactual explanations to help operators understand and trust algorithmic results, improving decision-making.

Contribution

It proposes counterfactual event fingerprints to enhance interpretability of fault diagnosis algorithms in water systems, bridging data-driven methods and operator intuition.

Findings

Effective in providing understandable explanations of diagnosis results

Improves operator trust and decision-making in water network management

Validated on a realistic benchmark scenario

Abstract

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.