Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis

by   Dilusha Weeraddana, et al.

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.


page 2

page 11


Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers

Data61 and Western Water worked collaboratively to apply engineering exp...

Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks

Water infrastructure in the United States is beginning to show its age, ...

Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis

Understanding performance and prioritizing resources for the maintenance...

Feasibility of roof top rainwater harvesting potential - A case study of South Indian University

Shortage of water source has been a major problem for rapidly growing ci...

First CE Matters: On the Importance of Long Term Properties on Memory Failure Prediction

Dynamic random access memory failures are a threat to the reliability of...

Deciding Not To Decide

Sometimes unexpected, novel, unconceivable events enter our lives. The c...

A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures

Analyzing the behavior of complex interdependent networks requires compl...

Please sign up or login with your details

Forgot password? Click here to reset