Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China

Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimizat...

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Bibliographic Details
Main Authors: Guangzhi Rong, Si Alu, Kaiwei Li, Yulin Su, Jiquan Zhang, Yichen Zhang, Tiantao Li
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Water
Subjects:
landslide susceptibility mapping
imbalanced sample
Bayesian optimization
random forest
gradient boosting decision tree
Online Access:https://www.mdpi.com/2073-4441/12/11/3066

Internet

https://www.mdpi.com/2073-4441/12/11/3066

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