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.

对于水分配网络中的污染源检测问题，我们提出了一种贝叶斯方法。假设污染是罕见的事件（在空间和时间上），我们尝试在少数感测到的节点中读取污染模式后，找到此类事件的最可能来源。该方法依赖于考虑离散时间节点的二进制干净/受污染状态的强大简化，因此该方法侧重于感测到的模式的时间结构，而不是集中度。结果，写入了离散变量的后验概率，并使用置信度传播算法计算了后验边际。生成的算法在给定的观测值上运行一次，并报告作为源的每个节点以及总共污染模式的概率。我们在Anytown模型上进行了测试，