Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China
The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF + LR, GBDT + LR, and XGB + LR), to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China.
The data that support the findings of this study are available on request from the corresponding author
This study was supported by grants from the Hunan Provincial Natural Resource Science and Technology Planning Program of China (Grant No. 2021-53), the National Natural Science Foundation of China (Grant Nos. 42072326 and 41772348), and the National Key Research and Development Program of China (Grant No. 2019YFC1805905).