Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
This work addresses leakage detection for water utilities to reduce water loss and contamination risks, but it is incremental as it applies existing drift detection methods to a new domain.
The paper tackled the problem of detecting leakages in water distribution networks by modeling them as concept drift and evaluating drift detection methods, finding that these methods can effectively identify leakages with varying sizes and detection times, and also proposed a technique for localizing leakages.
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.