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Landslides susceptibility assessment and risk mapping using logistic regression and geographical information system

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dc.citedby1
dc.contributor.authorBuslima F.en_US
dc.contributor.authorOmar R.C.en_US
dc.contributor.authorRoslan R.en_US
dc.contributor.authorBaharuddin I.N.Z.en_US
dc.contributor.authorSolemon B.en_US
dc.contributor.authorWahab W.A.en_US
dc.contributor.authorGunasagaran V.en_US
dc.contributor.authorid57205233997en_US
dc.contributor.authorid35753735300en_US
dc.contributor.authorid57159693200en_US
dc.contributor.authorid55812431300en_US
dc.contributor.authorid24832320000en_US
dc.contributor.authorid56040257700en_US
dc.contributor.authorid57211668683en_US
dc.date.accessioned2023-05-29T07:22:56Z
dc.date.available2023-05-29T07:22:56Z
dc.date.issued2019
dc.description.abstractRapid development in the agriculture sector, land clearing, and construction have a great impact on the surface and soils structure especially in the mountainous area, for example, Cameron Highlands. These activities coupled with natural triggering factors like aspect of slope, elevation,geology, angle of slope, curvature, and rainfall may lead to serious geological hazard such as landslides. Cameron Highlands is one of the regions that is known to be susceptible to landslides. A study was carried out to classifysusceptible areas and guide tothe risk management. In this study, Logistics Regression (LR) using Geographical Information System (GIS) was applied to assess the susceptibility oflandslidesat Cameron Highlands. Ten (10) landslide contributing factors are taking into consideration including elevation, aspect, geology, slope, curvature,land use, distance from the fault, distance from drainage and road as well as rainfall. Based on the result, the LR approach obtained 82.5% landslides prediction accuracy and considered as a good result for the prediction. With the right information and updates from the landslides susceptibility map, it will assist the local authority in mitigating, treating and controlling this natural hazard at an early stage before any landslide happen. � 2019 SERSC.en_US
dc.description.natureFinalen_US
dc.identifier.epage358
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85081190613
dc.identifier.spage350
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081190613&partnerID=40&md5=11dca9651c8c6faecda2cf26c9667b46
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24352
dc.identifier.volume28
dc.publisherScience and Engineering Research Support Societyen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Science and Technology
dc.titleLandslides susceptibility assessment and risk mapping using logistic regression and geographical information systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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