  Australian water infrastructure is more than a hundred years old, thus has
begun to show its age through water main failures. Our work concerns
approximately half a million pipelines across major Australian cities that
deliver water to houses and businesses, serving over five million customers.
Failures on these buried assets cause damage to properties and water supply
disruptions. We applied Machine Learning techniques to find a cost-effective
solution to the pipe failure problem in these Australian cities, where on
average 1500 of water main failures occur each year. To achieve this objective,
we construct a detailed picture and understanding of the behaviour of the water
pipe network by developing a Machine Learning model to assess and predict the
failure likelihood of water main breaking using historical failure records,
descriptors of pipes and other environmental factors. Our results indicate that
our system incorporating a nonparametric survival analysis technique called
"Random Survival Forest" outperforms several popular algorithms and expert
heuristics in long-term prediction. In addition, we construct a statistical
inference technique to quantify the uncertainty associated with the long-term
predictions.
