ABSTRACT

Water supply pipes age, deteriorate and break, which puts at risk the continuous provision of safe potable water endangering the public health in cities. Risk management methods are increasingly applied to optimise the capital investment for pipe replacement and rehabilitation, taking into account the probability and hydraulic impact of pipe breaks. As part of this process, however, historic pipe break data and statistical methods should be utilised to gather causal insights for past breaks to inform operational changes and/or capital investment decisions in order to reduce future breaks. This paper presents a comparative study of statistical and machine learning methods to carry out an exploratory causal analysis for historic pipe breaks in an operational water supply network. Regression models for count data and probabilistic models have been developed. The performance of these models was assessed and enhanced with the introduction of interactions and the inclusion of different network representations.

摘要

供水管道老化，恶化和断裂，使持续提供安全饮用水的危险受到威胁，危及城市的公共健康。考虑到管道破裂的可能性和液压影响，越来越多地采用风险管理方法来优化用于管道更换和修复的资本投资。但是，作为此过程的一部分，应使用历史性的管道中断数据和统计方法来收集过去中断的因果见解，以告知运营变更和/或资本投资决策，以减少将来的中断。本文对统计方法和机器学习方法进行了比较研究，以对运营供水网络中历史性管道断裂进行探索性因果分析。已经开发出用于计数数据的回归模型和概率模型。通过引入交互作用和包含不同的网络表示，评估并增强了这些模型的性能。