Spatiotemporal Landslide Susceptibility Mapping using Hybrid Machine Learning (BayesOpt-XGBoost) and GIS

This study is based on the hypothesis that landslide susceptibility on Penang Island changes over time in response to variations in topography, vegetation cover, and land use, and that a BayesOpt-XGBoost model can effectively capture these spatiotemporal dynamics with high predictive accuracy. Landslide and non-landslide grid units were extracted using an inventory of 443 landslide and 443 non-landslide points. Four primary conditioning factors, namely Digital Elevation Model (DEM), slope, land cover (LC), and Normalized Difference Vegetation Index (NDVI), were derived for the years 2014, 2017, and 2025. DEM and slope were generated from elevation data, NDVI was calculated from satellite imagery, and LC was obtained from classified land use maps. T