Flood factors frequency graph.

<div><p>Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic informa...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Tahmina Afrose Keya (20141592) (author)
مؤلفون آخرون:	Siventhiran S. Balakrishnan (20141595) (author), Maheswaran Solayappan (20141598) (author), Saravana Selvan Dheena Dhayalan (20141601) (author), Sreeramanan Subramaniam (466365) (author), Low Jun An (20141604) (author), Anthony Leela (20141607) (author), Kevin Fernandez (20141610) (author), Prahan Kumar (20141613) (author), A. Lokeshmaran (20141616) (author), Abhijit Vinodrao Boratne (20141619) (author), Mohd Tajuddin Abdullah (10147568) (author)
منشور في:	2024
الموضوعات:	Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified xlink "> malaysia standard statistical measures improve flood management geographic information system flood vulnerability assessment flood susceptibility models flood susceptibility mapping flood susceptibility map flood influencing factors expertise provide opportunities ensemble machine learning dense tree canopies class collectively made causing significant damage abundant flooded vegetation patterns ranking subsequently high &# 8217 pahang &# 8217 1 x64 ). fs ranking demonstrated &# 8217 high risk gis ). particularly pahang pahang found validation datasets technological advancements steep inclines sophisticated methods random forest primary attributes lying topography less susceptible land use land cover intense precipitation geospatial database flow direction flow accumulation feature selection central regions better prepared bare ground arcgis software algorithm based
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Flood factors frequency graph.

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