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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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