Extreme Gradient Boosting (XGBoost) and Random Forest (RF) Hybrid Ensemble with Bayesian Optimization in Landslide Susceptibility Mapping

Many models have been developed in the past for predicting landslide vulnerability. The generated models have certain shortcomings, especially when it comes to the problems of overfitting and overestimation. Therefore, the objective of this study is to assess and enhance the performance of Extreme G...

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Bibliographic Details

Main Author:	Dorothy, Anak Martin Atok
Format:	Thesis
Language:	en en en
Published:	UNIMAS 2025
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://ir.unimas.my/id/eprint/48367/5/DOROTHY_Student%20Declaration%20of%20Original%20Work.pdf http://ir.unimas.my/id/eprint/48367/7/Extreme%20Gradient%20Boosting%20%28XGBoost%29%20and%20Random%20Forest%20%28RF%29%20Hybrid%20Ensemble%20with%20Bayesian%20Optimization%20in%20Landslide%20Susceptibility%20Mapping%20%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/48367/6/Extreme%20Gradient%20Boosting%20%28XGBoost%29%20and%20Random%20Forest%20%28RF%29%20Hybrid%20Ensemble%20with%20Bayesian%20Optimization%20in%20Landslide%20Susceptibility%20Mapping.pdf http://ir.unimas.my/id/eprint/48367/
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