A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks

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.

KEYWORDS:

• Water supply systems
• pipe breaks
• statistical modelling
• machine learning
• causal inference

Acknowledgements

The work has been carried out in the Department of Civil and Environmental Engineering at Imperial College London. We would like to acknowledge the help and support of Dr Hossein Rezaei for compiling and sharing the pipe breaks data used in this study. This work has been supported by EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future).

Disclosure statement

The authors declare that they have no conflict of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, CK, upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed here