Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models.

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology, overburden depth, lithology, distance to rivers and distance to roads. These factors were instrumental in predicting the output variable, which was the probability of landslide occurrence. Statistical analysis of the models' performance indicated that the DG-RBFN model, with an Area Under ROC Curve (AUC) of 0.931, outperformed the other models. The AB-RBFN model achieved an AUC of 0.929, the MB-RBFN model had an AUC of 0.913, and the MLP model recorded an AUC of 0.926. These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model, single MLP model, and other ensemble models in preparing trustworthy landslide susceptibility maps, thereby enhancing land use planning and decision-making. [ABSTRACT FROM AUTHOR]