Optimizing Landslide Susceptibility Mapping: A Comparative Study of Ensemble Models and Forest by Penalizing Attributes

The present study introduces a novel approach to landslide susceptibility assessment by integrating the Forest Attribute Penalty (FPA) model with three ensemble algorithms—AdaBoost (AB), Rotation Forest (RF), and Random Subspace (RS)—and utilizing the Evidential Belief Function (EBF) to weight the classes of 16 landslide-related factors. To evaluate the performance of the developed methodology Yanchuan County, China, was chosen as appropriate study area. Three hundred and eleven landslide areas were identified through remote sensing and field investigations, which were randomly divided into 70% for model training and 30% for model evaluation, whereas sixteen landslide – related factors were considered, such as elevation, slope aspect, profile curvature, plan curvature, convergence index, slope length, terrain ruggedness index, topographic position index, distance to roads, distance to rivers, NDVI, land use, soil, rainfall, and lithology. EBF was employed to analyze the spatial correlation between these factors and landslide occurrences, providing the class weights of each factor for the implementation of FPA and the ensemble models. The next step involved the generation of the landslide susceptibility maps based on the models, with findings showing that more than half of the study area is classified as very low susceptibility. Model performance was assessed using receiver operating characteristic (ROC) curves and other statistical metrics, with the RFFPA model achieving the highest predictive ability, with AUC values of 0.878 and 0.890 for training and validation datasets, respectively. The AFPA and RSFPA hybrid models, however, demonstrated weaker predictive abilities compared to the FPA model. The study highlights the importance of optimizing model performance and evaluating the suitability of ensemble approaches, emphasizing the role of topographical and environmental settings in influencing model accuracy. The use of EBF for weight calculation proved crucial in improving model outcomes, suggesting that this approach could be further refined and adapted to other regions with similar geomorphological settings for better land use planning and risk management.