Landslide susceptibility assessment using feature selection-based machine learning models: RF, SVM, and LR applications in Rif, Morocco

Landslides pose significant risks in the mountainous Rif region of Morocco, particularly in the Oued Amter watershed, where prior studies rarely integrated advanced feature selection with machine learning to enhance prediction accuracy. This study employs Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) to develop a landslide susceptibility map, utilizing 1456 inventory points and identifying 13 key factors (stream, land use/land cover, elevation, and lithology), vi