Contamination source detection in water distribution networks using belief propagation

We present a Bayesian approach for the Contamination Source Detection problem in water distribution networks. Assuming that contamination is a rare event (in space and time), we try to locate the most probable source of such events after reading contamination patterns in few sensed nodes. The method relies on strong simplifications considering binary clean/contaminated states for nodes in discrete time, and therefore focuses on the time structure of the sensed patterns rather than on the concentration levels. As a result, a posterior probability over discrete variables is written, and posterior marginals are computed using belief propagation algorithm. The resulting algorithm runs once on a given observation and reports probabilities for each node being the source and for the contamination patterns altogether. We test it on Anytown model, proving its efficacy even when only a single sensed node is known.