Application of potential machine learning models in landslide susceptibility assessment: A case study of Van Yen district, Yen Bai province, Vietnam

Abstract

Landslides are natural hazards that cause significant damage to both property and human lives. This study employs potential machine learning models such as Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) to assess landslide susceptibility in Van Yen District, Yen Bai Province, Vietnam, that experiences a higher frequency of landslides compared to other localities in the region. The study incorporates thirteen input variables, including elevation, slope angle, aspect, plan curvature, profile curvature, Topographic Wetness Index (TWI), distance to faults, lithology, distance to roads, distance to rivers, land cover, rainfall, and Normalized Difference Vegetation Index (NDVI). To construct the models, landslide statistics reports were utilized, consisting of 302 landslide points collected through field surveys and 52 landslide points determined using Radar Sentinel-1 images. The Google Earth Engine cloud computing platform is utilized for constructing the landslide susceptibility models. The outcome of the research is a landslide susceptibility map with five levels: very low, low, moderate, high, and very high. The Area Under the Curve (AUC) is used as a metric to evaluate the performance of all three models. The findings indicate that, besides similarities observed in landslide susceptibility maps for previously occurred landslides, the Random Forest model demonstrates a favorable performance compared to the other models, with an AUC of 0.883.