Advancements in mapping landslide susceptibility in Bafoussam and its surroundings area using multi-criteria decision analysis, statistical methods, and machine learning models

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Naïve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouaché, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management. [Display omitted] •A comprehensive landslide susceptibility mapping (LSM) was conducted in the west region of Cameroon, focusing on Bafoussam and its surroundings.•The study utilized a combination of Multi-Criteria Decision Analysis models, statistical methods, and machine learning algorithms for a robust assessment of landslide risk.•Susceptibility maps classified the area into five risk zones, with the centre and northwestern regions identified as high-risk areas.•Validation using the AUC/ROC method showed the Logistic Regression and Naïve Bayes methods had the highest accuracy (82.7% and 84.1%, respectively).•The study underscores the need for a tailored approach to LSM selection and the importance of integrating multiple methodologies for comprehensive risk assessment and management.