Tree Boosting Methods Comparison for Landslide Susceptibility Maps, Case Study: Kejajar, Wonosobo

Landslides in Indonesia occur almost every year and cause large material losses. Early prevention by creating a landslide susceptibility map is one way to anticipate losses due to landslides. The search for the best method for predicting landslides using machine learning with several tree boosting methods has been carried out, but the comparison between the tree boosting methods is unknown. This study aims to compare the tree boosting methods in their use for creating landslide susceptibility maps. The case study used in this research is Kejajar District, Wonosobo. There are 25 data features used to determine landslide. The landslide data in this study is 84 polygons. The tree boosting methods used include XGBoost, LGBM, Adaboost and Catboost. Hyperparameter tuning and k-fold cross validation were used to get the best model. The results of the comparison show that LGBM is the best method with accuracy, recall, f1 score, and ROC AUC values of 0.9903, 0.9360, 0.9154, and 0.9648 respectively. It indicates that the boosting method using LGBM can provide good results for creating a landslide susceptibility map.