Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers

Weeraddana, D Liang, B

Li, Z

Wang, Y Chen, F Bonazzi, L Phillips, D Saxena, N

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Field	Value	Language
dc.contributor.author	Weeraddana, D
dc.contributor.author	Liang, B https://orcid.org/0000-0002-6605-2167
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Wang, Y
dc.contributor.author	Chen, F
dc.contributor.author	Bonazzi, L
dc.contributor.author	Phillips, D
dc.contributor.author	Saxena, N
dc.date.accessioned	2021-04-27T06:04:33Z
dc.date.available	2021-04-27T06:04:33Z
dc.date.issued	2020-06-05
dc.identifier.citation	arXiv, 2020
dc.identifier.uri	http://hdl.handle.net/10453/148432
dc.language	en
dc.relation.ispartof	arXiv
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers
dc.type	Preprint
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	open_access	*
dc.date.updated	2021-04-27T06:04:31Z

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