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

• URL: http://arxiv.org/abs/2006.03385v1
• Date: Fri, 5 Jun 2020 11:44:02 GMT
• Title: Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers
• Authors: Dilusha Weeraddana, Bin Liang, Zhidong Li, Yang Wang, Fang Chen, Livia Bonazzi, Dean Phillips, Nitin Saxena
• Abstract summary: Data61 and Western Water applied engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne. We constructed a detailed picture and understanding of the behaviour of the water pipe network. We developed a Machine Learning system to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes, and other environmental factors.
• Score: 9.523624462369918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year. To achieve this objective, we constructed a detailed picture and understanding of the behaviour of the water pipe network by 1) discovering the underlying drivers of water main breaks, and 2) developing a Machine Learning system to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes, and other environmental factors. The ensuing results open up an avenue for Western Water to identify the priority of pipe renewals

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