Integrating Remote Sensing, GIS and Machine Learning Approaches in Evaluation of Landslide Susceptibility in Mountainous Region of Nghe An Province, Vietnam

Abstract

Abstract This study applied remote sensing methods combining GIS and machine learning (ML) 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 (TPI), 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. The landslide estimation algorithms which derived from the machine learning approach including Support Vector Machine, Random Forest, and Logistic Regression have been investigated with 11 input layers and field survey training data. The results indicated that among the causative parameters of landslides in the study area, the most important factor was the Standardized Precipitation Index, derived from the rainfall data. Additionally, traffic, terrain slope, and elevation were also significant factors. In terms of the landslide estimation algorithms, the Random Forest model exhibited the highest accuracy for mapping landslide susceptibility in the western mountainous region of Nghe An province, with a correlation coefficient (R2) of 0.97. The research findings demonstrate the effectiveness of integrating remote sensing, GIS, and ML techniques for landslide research in mountainous areas of Vietnam. This approach provides valuable insights on landslide susceptibility, and a better understanding of landslide dynamics in the study area.