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    Flood Susceptibility Assessment in the Jhelum Basin Using Machine Learning and GIS Techniques

    Source: Natural Hazards Review:;2026:;Volume ( 027 ):;issue: 003::page 04026012-1
    Author:
    Hassan, Safeena
    ,
    Samantaray, Sandeep
    ,
    Annayat, Wajahat
    ,
    Satapathy, Deba Prakash
    DOI: 10.1061/NHREFO.NHENG-2672
    Publisher: American Society of Civil Engineers
    Abstract: AbstractFlood susceptibility assessment is critical for disaster risk reduction, particularly in flood-prone areas like the Jhelum Basin. This study integrates machine learning techniques, including random forest (RF), artificial neural networks (ANN), ...
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      Flood Susceptibility Assessment in the Jhelum Basin Using Machine Learning and GIS Techniques

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4314034
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    contributor authorHassan, Safeena
    contributor authorSamantaray, Sandeep
    contributor authorAnnayat, Wajahat
    contributor authorSatapathy, Deba Prakash
    date accessioned2026-08-20T21:09:13Z
    date available2026-08-20T21:09:13Z
    date copyright2026/05/27
    date issued2026
    identifier otherNHREFO.NHENG-2672.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314034
    description abstractAbstractFlood susceptibility assessment is critical for disaster risk reduction, particularly in flood-prone areas like the Jhelum Basin. This study integrates machine learning techniques, including random forest (RF), artificial neural networks (ANN), ...
    publisherAmerican Society of Civil Engineers
    titleFlood Susceptibility Assessment in the Jhelum Basin Using Machine Learning and GIS Techniques
    typeJournal Article
    journal volume27
    journal issue3
    journal titleNatural Hazards Review
    identifier doi10.1061/NHREFO.NHENG-2672
    journal fristpage04026012-1
    journal lastpage04026012-16
    page16
    treeNatural Hazards Review:;2026:;Volume ( 027 ):;issue: 003
    contenttypeFulltext
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