  Facing climate change, the already limited availability of drinking water
will decrease in the future rendering drinking water an increasingly scarce
resource. Considerable amounts of it are lost through leakages in water
transportation and distribution networks. Thus, anomaly detection and
localization, in particular for leakages, are crucial but challenging tasks due
to the complex interactions and changing demands in water distribution
networks. In this work, we analyze the effects of anomalies on the dynamics of
critical infrastructure systems by modeling the networks employing Bayesian
networks. We then discuss how the problem is connected to and can be considered
through the lens of concept drift. In particular, we argue that model-based
explanations of concept drift are a promising tool for localizing anomalies
given limited information about the network. The methodology is experimentally
evaluated using realistic benchmark scenarios. To showcase that our methodology
applies to critical infrastructure more generally, in addition to considering
leakages and sensor faults in water systems, we showcase the suitability of the
derived technique to localize sensor faults in power systems.
