Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination

Abstract

A Bayesian belief network (BBN) methodology is proposed for combining evidence to better characterize contamination events and reduce false positive sensor detections in drinking water distribution systems. A BBN is developed that integrates sensor data with other validating evidence of contamination scenarios. This network is used to graphically express the causal relationships between events such as operational changes or a true contaminant release and consequent observable evidence in an example distribution system. In the BBN methodology proposed here, multiple computer simulations of contaminant transport are used to estimate the prior probabilities of a positive sensor detection. These simulations are run over multiple combinations of possible source locations and initial mass injections for a conservative solute. This approach provides insight into the effect of uncertainties in source mass and location on the detection probability of the sensors. In addition, the simulations identify the upstream nodes that are more likely to result in positive detections. The BBN incorporates the probabilities that result from these simulations, and the network is updated to reflect three demonstration scenarios—a false positive and two true positive sensor detections.