Machine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities

dc.contributor.author	Gupta, Himadri Sen
dc.contributor.author	Nofal, Omar N.
dc.contributor.author	González, Andrés D.
dc.contributor.author	Nicholson, Charles D.
dc.contributor.author	van de Lindt, John W.
dc.date.accessioned	2026-08-20T21:07:38Z
dc.date.available	2026-08-20T21:07:38Z
dc.date.copyright	2025/07/15
dc.date.issued	2025
dc.identifier.other	NHREFO.NHENG-2330.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313993
dc.description.abstract	AbstractFlood-prone communities face severe economic and structural losses, necessitating strategic building retrofitting to enhance resilience. However, selecting the most effective mitigation strategies remains challenging due to financial constraints ...
dc.publisher	American Society of Civil Engineers
dc.title	Machine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities
dc.type	Journal Article
dc.journal.volume	26
dc.journal.issue	4
dc.journal.title	Natural Hazards Review
dc.identifier.doi	10.1061/NHREFO.NHENG-2330
dc.journal.fristpage	04025044-1
dc.journal.lastpage	04025044-20
dc.page	20
dc.tree	Natural Hazards Review:;2025:;Volume ( 026 ):;issue: 004
dc.contenttype	Fulltext