The Impact of Feature Selection on XGBoost Performance in Landslide Susceptibility Mapping Using an Extended Set of Features: A Case Study from Southern Poland

Landslides are among the most frequent and dangerous natural hazards, posing serious threats to life and infrastructure. To mitigate their impacts, landslide susceptibility mapping (LSM) plays a crucial role by identifying areas prone to future landslide occurrences. This study aimed to assess how the choice of feature selection methods influences the performance of LSM models based on the eXtreme Gradient Boosting (XGBoost) algorithm when an extended set of input variables is used. Two study areas located in Southern Poland, called Biały Dunajec and Rożnów, were selected for analysis. These regions differ in terrain, elevation, and environmental characteristics and are situated approximately 65 km apart. Three widely used feature selection techniques were applied: the Pearson correlation coefficient (PCC), symmetrical uncertainty (SU), and analysis of variance (ANOVA). For each method, XGBoost models were trained and evaluated using multiple performance metrics, including the area under the curve (AUC), overall accuracy, precision, recall, and F1-score. The highest AUC values were achieved using the PCC method: 0.985 for Biały Dunajec and 0.983 for Rożnów. The best overall performance (accuracy of 0.93, recall of 0.94, and F1-score of 0.79) was obtained for the Rożnów case study using PCC features. These findings highlight that, when a comprehensive set of input variables is used, the exclusion of less informative features has little effect on model accuracy, as their information is largely preserved within the retained features.