Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks

TL;DR

This paper explores the application of concept drift detection methods to identify leakages in water distribution networks, addressing challenges like small leak detection and temporal data dependencies.

Contribution

It systematically evaluates model-loss and distribution-based drift detection methods for water leakages and introduces a novel technique for localizing leakages based on drift detection.

Findings

Distribution-based methods effectively detect large leakages.

Model-loss-based methods are sensitive to small leakages.

Proposed localization technique improves pinpointing leakages.

Abstract

Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamics of water distribution networks. In particular, small leakages are hard to detect. From a machine-learning perspective, leakages can be modeled as concept drift. Thus, a wide variety of drift detection schemes seems to be a suitable choice for detecting leakages. In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection. We additionally discuss the issue of temporal dependencies in the data and propose a way to cope with it when applying distribution-based detection. We evaluate different methods systematically for leakages of different sizes and detection times. Additionally, we propose a first drift-detection-based technique for localizing leakages.