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

本研究基于如下研究假设：槟城岛的滑坡敏感性随时间推移发生动态变化，其变化响应于地形、植被覆盖与土地利用的差异；且BayesOpt-XGBoost模型可有效捕捉此类时空动态特征，并具备较高的预测精度。本研究基于包含443个滑坡点与443个非滑坡点的滑坡编目数据集，提取得到滑坡与非滑坡网格单元。本研究选取四类核心孕灾因子，即数字高程模型（Digital Elevation Model，DEM）、坡度、土地覆盖（Land Cover，LC）与归一化差分植被指数（Normalized Difference Vegetation Index，NDVI），并获取了2014年、2017年与2025年的各因子对应数据。其中，DEM与坡度由高程数据生成，NDVI通过卫星影像计算得到，LC则源自分类土地利用图件。T