CatBoost-Based Landslide Susceptibility Modeling Using Landsat 8 Imagery: A Case Study from Phuoc Son, Vietnam

Abstract

The mountainous terrain and monsoon-dominated climate of Central Vietnam make the region highly susceptible to rainfall-induced landslides, particularly in the Phuoc Son area, where steep slopes, complex geological conditions, and intense precipitation frequently trigger slope failures. This study develops a landslide susceptibility model using the CatBoost machine learning algorithm by integrating Landsat 8-derived surface indicators with key geo-environmental factors, including topographic, geological, hydrological, and vegetation-related parameters such as the Normalized Difference Vegetation Index (NDVI). The predictive performance of the proposed model was evaluated and compared with four widely used approaches, namely Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Deep Neural Network (DNN). Model accuracy was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). The results demonstrated that CatBoost outperformed all benchmark models, achieving AUCs of 0.97 and 0.93 on the training and testing datasets, respectively, indicating excellent predictive capability and strong generalization. The resulting landslide susceptibility maps effectively delineated areas with varying levels of landslide risk and provided enhanced spatial accuracy in identifying highly susceptible zones. These findings highlight the effectiveness of integrating remote sensing data with advanced ensemble machine learning techniques for landslide susceptibility assessment in tropical mountainous environments and provide a reliable scientific basis for hazard mitigation, land-use planning, and disaster risk management in Central Vietnam.