  Understanding performance and prioritizing resources for the maintenance of
the drinking-water pipe network throughout its life-cycle is a key part of
water asset management. Renovation of this vital network is generally hindered
by the difficulty or impossibility to gain physical access to the pipes. We
study a statistical and machine learning framework for the prediction of water
pipe failures. We employ classical and modern classifiers for a short-term
prediction and survival analysis to provide a broader perspective and long-term
forecast, usually needed for the economic analysis of the renovation. To enrich
these models, we introduce new predictors based on water distribution domain
knowledge and employ a modern oversampling technique to remedy the high
imbalance coming from the few failures observed each year. For our case study,
we use a dataset containing the failure records of all pipes within the water
distribution network in Barcelona, Spain. The results shed light on the effect
of important risk factors, such as pipe geometry, age, material, and soil
cover, among others, and can help utility managers conduct more informed
predictive maintenance tasks.

Signal Processing;Machine Learning;Machine Learning