Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis
This work addresses predictive maintenance for water utility managers by enabling more informed resource prioritization, though it is incremental as it applies existing methods with domain-specific enhancements.
The paper tackled the problem of predicting water pipe failures by developing a statistical and machine learning framework that combines classifiers for short-term prediction and survival analysis for long-term forecasting, using a dataset from Barcelona, Spain, to identify key risk factors like pipe geometry, age, material, and soil cover.
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.