Machine learning for water mains maintenance

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
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	2022-09-13T23:52:54Z
dc.date.available	2022-09-13T23:52:54Z
dc.date.issued	2019-01-01
dc.identifier.citation	Water e-Journal, 2019, 4, (3), pp. 1-13
dc.identifier.issn	2206-1991
dc.identifier.issn	2206-1991
dc.identifier.uri	http://hdl.handle.net/10453/161806
dc.language	en
dc.publisher	Australian Water Association
dc.relation.ispartof	Water e-Journal
dc.relation.isbasedon	10.21139/wej.2019.017
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Machine learning for water mains maintenance
dc.type	Journal Article
utslib.citation.volume	4
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	closed_access	*
dc.date.updated	2022-09-13T23:52:31Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	4
utslib.citation.issue	3

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