Assessment of Landslide Susceptibility and Settlement Exposure via Geospatial Techniques in Bulambuli Distrcit, Eastern Uganda

Landslide susceptibility is a significant concern in Elgon County, Uganda, particularly during the rainy season. This vulnerability is attributable to several factors, including steep slopes, fertile soils, and dense settlements on volcanic ridges. Landslide susceptibility maps are important in mitigating the risk particularly at the local level. The objectives of this study were 1) to model landslide susceptibility via an interpretable machine-learning model, 2) to identify the most influential factors for landslide susceptibility in the study area, and 3) to assess the exposure of settlements to landslide risk. This study employed the XGBoost model trained on nine conditioning factors via GIS data. Exposure analysis was performed through the zonal statistics and spatial overlay of the landslide susceptibility map with the settlement footprint data and classified into four risk exposure classes. The results show that the XGBoost model attained an AUC of 95.2%, indicating its precision. The results further revealed that approximately 50% of the slopes are susceptible to landslides and that 76% of the settlements in the study area are highly exposed to landslide risk. Bulugunya, Sisiyi, Lusha, and Buginyanya subcounties located on the middle slopes are the most susceptible areas in Elgon County and have relatively high settlement exposure because of the overlap of dense settlements with unstable terrain. The SHAP analysis identified slope, elevation, and the NDVI as the key influencing factors of susceptibility. This study highlights the importance of conducting detailed, local-scale landslide susceptibility and risk exposure mapping as necessary for risk and vulnerability assessment. The generation of such maps has the potential to inform land-use planning and risk-reduction strategies, thus offering significant advantages over regional models. Furthermore, by interpreting the XGBoost model, this study provides valuable insights into the decision-making processes of machine learning models, promoting their practical application in designing appropriate disaster mitigation plans.