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

    Source: Natural Hazards Review:;2025:;Volume ( 026 ):;issue: 004::page 04025044-1
    Author:
    Gupta, Himadri Sen
    ,
    Nofal, Omar N.
    ,
    González, Andrés D.
    ,
    Nicholson, Charles D.
    ,
    van de Lindt, John W.
    DOI: 10.1061/NHREFO.NHENG-2330
    Publisher: American Society of Civil Engineers
    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 ...
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      Machine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4313993
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    contributor authorGupta, Himadri Sen
    contributor authorNofal, Omar N.
    contributor authorGonzález, Andrés D.
    contributor authorNicholson, Charles D.
    contributor authorvan de Lindt, John W.
    date accessioned2026-08-20T21:07:38Z
    date available2026-08-20T21:07:38Z
    date copyright2025/07/15
    date issued2025
    identifier otherNHREFO.NHENG-2330.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313993
    description abstractAbstractFlood-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 ...
    publisherAmerican Society of Civil Engineers
    titleMachine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities
    typeJournal Article
    journal volume26
    journal issue4
    journal titleNatural Hazards Review
    identifier doi10.1061/NHREFO.NHENG-2330
    journal fristpage04025044-1
    journal lastpage04025044-20
    page20
    treeNatural Hazards Review:;2025:;Volume ( 026 ):;issue: 004
    contenttypeFulltext
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