  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.
