Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

Sustainable landslide mitigation requires appropriate approaches to predict susceptible zones. This study compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naïve-Bayes Tree (NBT) in predicting landslide susceptibility for the upper Nyabarongo catchment (Rwanda). 196 past...

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Bibliographic Details
Main Authors: Jean Baptiste Nsengiyumva, Roberto Valentino
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
landslide
gis
ipar-rwanda
nyabarongo
disaster-risk
Online Access:http://dx.doi.org/10.1080/19475705.2020.1785555

Internet

http://dx.doi.org/10.1080/19475705.2020.1785555

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