Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples

Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples

The present research intended to assess the robustness of three popular machine learning models, i.e. random forest (RF), boosted regression tree (BRT) and naïve bayes (NB) in spatial gully erosion susceptibility modelling in Jainti River basin, India. A gully inventory map of 208 gullies was prepar...

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Bibliographic Details
Main Authors: Tusar Kanti Hembram, Sunil Saha, Biswajeet Pradhan, Khairul Nizam Abdul Maulud, Abdullah M. Alamri
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
machine learning
gully erosion
gis
altered gully samples
india
Online Access:http://dx.doi.org/10.1080/19475705.2021.1890644

Internet

http://dx.doi.org/10.1080/19475705.2021.1890644

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