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

• URL: http://arxiv.org/abs/2401.01733v1
• Date: Wed, 3 Jan 2024 13:12:04 GMT
• Title: Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
• Authors: Valerie Vaquet, Fabian Hinder, Barbara Hammer
• Abstract summary: 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. From a machine-learning perspective, leakages can be modeled as concept drift.
• Score: 7.0072935721154614
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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