Show simple item record

dc.contributor.authorLi, Zilin
dc.contributor.authorLiu, Guoxuan
dc.contributor.authorLiu, Haixing
dc.contributor.authorSavic, Dragan
dc.contributor.authorFu, Guangtao
dc.date.accessioned2026-08-20T21:05:49Z
dc.date.available2026-08-20T21:05:49Z
dc.date.copyright2025/08/05
dc.date.issued2025
dc.identifier.otherJWRMD5.WRENG-6950.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313943
dc.description.abstractAbstractAccurate short-term water demand forecasting is essential for the operational management of water distribution systems. Previous research on the use of machine learning approaches to improve predictive accuracy has tackled issues of model ...
dc.publisherAmerican Society of Civil Engineers
dc.titleUsage of Multisource Data through Transfer Learning to Improve Short-Term Water Demand Forecasting
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue10
dc.journal.titleJournal of Water Resources Planning and Management
dc.identifier.doi10.1061/JWRMD5.WRENG-6950
dc.journal.fristpage04025053-1
dc.journal.lastpage04025053-13
dc.page13
dc.treeJournal of Water Resources Planning and Management:;2025:;Volume ( 151 ):;issue: 010
dc.contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record