A recent SEED Group paper, "Bayesian Belief Networks for Predicting Drinking Water Distribution System Pipe Breaks" was presented at PSAM11/ESREL12 in Helsinki, Finland. This peer-reviewed conference paper was co-authored by Dr. Francis with JHU collaborators Dr. Seth Guikema and Lucas Henneman. The abstract of this paper follows:
In this project, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. BBN modeling is a critical step towards real-time distribution system management. Development of expert systems for analyzing real-time data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate real-time distribution system monitoring and management. Our model will be based on pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. The expert model is learned using a conditional independence test method, a score-based method, and a hybrid method, then subjected to 10-fold cross validation based on log-likelihood scores.
A short report from PSAM11/ESREL12 will follow in a later post.