Bayesian approach for real-time probabilistic contamination source identification

Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes' rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to changes in sensor signals. In terms of intrusion events, the beta-binomial approach was more selective than the Bayes' rule approach in identifying potential source node-time pairs and provided the flexibility to account for multiple possible injection locations.