Explainable Machine-Learning Leak Identification Framework for Water Distribution Networks

dc.contributor.author	Liu, Rongsheng
dc.contributor.author	Zayed, Tarek
dc.contributor.author	Xiao, Rui
dc.date.accessioned	2026-08-20T21:22:35Z
dc.date.available	2026-08-20T21:22:35Z
dc.date.copyright	2025/07/31
dc.date.issued	2025
dc.identifier.other	JCCEE5.CPENG-6119.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314358
dc.description.abstract	AbstractLeak identification in urban water distribution networks (WDNs) is a critical task with potential consequences. Acoustic-based convolutional neural networks (CNNs) have been widely employed for leak identification, leveraging its advantages with ...
dc.publisher	American Society of Civil Engineers
dc.title	Explainable Machine-Learning Leak Identification Framework for Water Distribution Networks
dc.type	Journal Article
dc.journal.volume	39
dc.journal.issue	6
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-6119
dc.journal.fristpage	04025090-1
dc.journal.lastpage	04025090-19
dc.page	19
dc.tree	Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 006
dc.contenttype	Fulltext