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dc.contributor.authorFang, Xin
dc.contributor.authorLi, Heng
dc.contributor.authorZhang, Sherong
dc.contributor.authorLiu, Kang
dc.contributor.authorWang, Xiaohua
dc.contributor.authorWang, Chao
dc.date.accessioned2026-08-20T21:24:15Z
dc.date.available2026-08-20T21:24:15Z
dc.date.copyright2025/09/27
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-6533.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314396
dc.description.abstractAbstractExtreme climate events are becoming increasingly frequent, and flood disasters have been one of the most frequent and devastating forms of such events, threatening the lives and property of coastal residents. To reduce the potential costs to ...Practical ApplicationsThe hybrid model developed in this study has significant practical implications for flood risk management and emergency response. By integrating hydrodynamic analysis with advanced deep learning techniques, the model enables real-...
dc.publisherAmerican Society of Civil Engineers
dc.titleIntegrating Hydrodynamic Mechanisms and Deep Learning for Real-Time Flood Inundation Forecasting
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue1
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6533
dc.journal.fristpage04025121-1
dc.journal.lastpage04025121-15
dc.page15
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 001
dc.contenttypeFulltext


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