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Landslide Susceptibility Mapping Using Multi-Source Geospatial Data and XGBoost
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DOI：10.3390/rs18142270.png)
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Abstract
En 中文
Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning.
Keywords:
landslide susceptibility assessment
multi-source geospatial data
XGBoost
SHAP interpretability analysis