Bài báo khoa học

This study applied remote sensing methods combining GIS and machine learning in landslide assessment and zonation for the western mountainous area of Nghe An province, Vietnam. Factors affecting landslide susceptibility are analyzed and included in the assessment model including terrain elevation, slope, aspect, flow accumulation, geomorphology, profile curvature, Topographic Position Index, fault density, road density, rainfall and land use. A field survey was conducted on July, 2023 to collect the ground truth data of landslide areas in Nghe An and used as input for the training and validating process of landslide model with ratios of 70 and 30 percentage. Machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) have been investigated with 11 input layers and field survey training data. Research results show that the RF model achieves the highest accuracy for landslide susceptibility assessment in the western mountainous region of Nghe An, with correlation coefficient R2 of 0.97. Research results have demonstrated the effectiveness of integrating remote sensing, GIS and machine learning in landslide research for mountainous areas in Vietnam.