Imperial College London
Alternate

DrIvanStoianov

Faculty of Engineering, Department of Civil and Environmental Engineering

Reader in Water Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6035ivan.stoianov Website

 
 
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Assistant

 

Miss Judith Barritt +44 (0)20 7594 5967

 
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Location

 

408Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Konstantinou:2020:10.1080/1573062X.2020.1800758,
author = {Konstantinou, C and Stoianov, I},
doi = {10.1080/1573062X.2020.1800758},
journal = {URBAN WATER JOURNAL},
pages = {534--548},
title = {A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks},
url = {http://dx.doi.org/10.1080/1573062X.2020.1800758},
volume = {17},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Konstantinou,C
AU  - Stoianov,I
DO  - 10.1080/1573062X.2020.1800758
EP  - 548
PY  - 2020///
SN  - 1573-062X
SP  - 534
TI  - A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks
T2  - URBAN WATER JOURNAL
UR  - http://dx.doi.org/10.1080/1573062X.2020.1800758
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000555649000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/1573062X.2020.1800758
VL  - 17
ER  -
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