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Scalable Machine Learning–Based Approach for Pressure Sensor Placement and Burst Detection/Localization in Large Water Distribution Networks

dc.contributor.authorLo Presti, J.
dc.contributor.authorGiudicianni, C.
dc.contributor.authorGaluppini, G.
dc.contributor.authorToffanin, C.
dc.contributor.authorMagni, L.
dc.contributor.authorCreaco, E.
dc.date.accessioned2026-08-20T21:06:39Z
dc.date.available2026-08-20T21:06:39Z
dc.date.copyright2026/04/27
dc.date.issued2026
dc.identifier.otherJWRMD5.WRENG-7134.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313965
dc.description.abstractAbstractA novel methodology for burst detection/localization and pressure sensor placement in water distribution networks (WDNs) is proposed, by combining machine learning and graph theory algorithms. After a preliminary clustering of the WDN graph ...
dc.publisherAmerican Society of Civil Engineers
dc.titleScalable Machine Learning–Based Approach for Pressure Sensor Placement and Burst Detection/Localization in Large Water Distribution Networks
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue7
dc.journal.titleJournal of Water Resources Planning and Management
dc.identifier.doi10.1061/JWRMD5.WRENG-7134
dc.journal.fristpage04026014-1
dc.journal.lastpage04026014-15
dc.page15
dc.treeJournal of Water Resources Planning and Management:;2026:;Volume ( 152 ):;issue: 007
dc.contenttypeFulltext


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