Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin

Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin

Predicting landslides has become a critical global challenge for promoting sustainable development in mountainous regions. This study conducts a comparative analysis of landslide susceptibility maps (L.S.M.s) generated using two GIS-based data-driven bivariate statistical models: (a) Frequency Ratio...

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Bibliographic Details
Main Authors: Indrajit Poddar, Ranjan Roy
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Quaternary Science Advances
Subjects:
Landslide susceptibility
Triggering factor
Evidential belief function (EBF)
Frequency ratio (FR)
Teesta River
Himalayas
Online Access:http://www.sciencedirect.com/science/article/pii/S2666033423000825

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http://www.sciencedirect.com/science/article/pii/S2666033423000825

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