论文标题
利用机器学习来通过了解管道故障驱动器来防止水主断裂
Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers
论文作者
论文摘要
Data61和Western Water合作运用工程专业知识和机器学习工具，以找到墨尔本以西地区的管道故障问题的成本效益解决方案，平均每年发生400个水主要故障。为了实现这一目标，我们通过1）发现了水管主要断裂的潜在驱动因素，以及2）开发机器学习系统，以评估和预测使用历史故障记录，管道描述符，管道的描述者和其他环境因素的失败可能性。随之而来的结果为西方水开辟了一条途径，以确定管道更新的优先级
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
