Bayesian Localization of Water Distribution System Contamination Intrusion Events Using Inline Mobile Sensor Data

Abstract

The intrusion of a foreign substance into the water distribution system represents a serious threat to public health. Large-scale water distribution systems serve thousands of consumers who may be put at risk to exposure and ingestion of potentially harmful substances. For an authority managing a water distribution system, it is important to (1) detect a potential contamination, and (2) locate the point of intrusion. However, points of known water quality data are expected to be sparsely distributed throughout the water distribution system, and may not provide sufficient data to quickly and accurately localize a contamination event. In this work, an inline mobile sensor was employed for the contamination event localization task in a Bayesian framework, such that the water quality data acquired by the mobile sensor were used to update the contamination intrusion location probabilities in the water distribution system. Using the Bayesian localization method was shown to improve the localization accuracy of a contamination event, with substantial improvements in the precision of localization.