Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
This work addresses the critical issue of water supply disruptions and property damage for over five million customers in Australian cities, representing an incremental improvement with a novel application of survival analysis to a specific domain.
The researchers tackled the problem of predicting water main failures in aging Australian infrastructure by developing a machine learning model using historical data and environmental factors, achieving results that outperformed existing algorithms and heuristics in long-term prediction.
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.