Many water utilities are facing a crisis of aging infrastructure. Aging pipes are deteriorating, and pipe breaks are increasing. A variety of pipe break prediction models have been developed to identifying which pipes are most likely to break next, in order to assist utilities in prioritizing pipe replacement. This paper investigates the role of data in pipe break prediction model accuracy. A gradient boosting decision tree machine learning model, a Weibull proportional hazard probabilistic model and two ranking models (based on ‘age of pipe’ and ‘previous-break’) were calibrated using a various number of pipes, years of break records and input variables. The results indicate how the different model types are impacted by data limitations. Overall, this study finds the Age-based approach to be inaccurate, and the XGBoost machine learning model demonstrates superior predictive capability when the training dataset contains more than 5 years of break records and 2,000 or more pipes.

许多自来水公司都面临着基础设施老化的危机。老化的管道正在恶化，并且管道的断裂处在增加。已经开发出各种管道破裂预测模型，以识别接下来最有可能破裂的管道，以帮助公用事业机构优先进行管道更换。本文研究了数据在管道破裂预测模型准确性中的作用。使用各种数量的管道，断裂记录的年份和输入变量来校准梯度增强决策树机器学习模型，Weibull比例风险概率模型和两个排名模型（基于“管道的年龄”和“先前断裂”） 。结果表明不同的模型类型如何受到数据限制的影响。总体而言，这项研究发现基于年龄的方法是不准确的，