Interpretability study of earthquake-induced landslide susceptibility combining dimensionality reduction and clustering

An earthquake of magnitude Ms5.8 struck Barkam City, Aba Prefecture, Sichuan Province, China, on the morning of 10 June 2022. This was followed by two additional earthquakes of magnitudes Ms6.0 and Ms5.2. The earthquakes triggered significant geological hazards, impacting Barkam City and surrounding areas. Using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) machine learning models, we assessed landslide susceptibility in Barkam City and identified key influencing factors. The study applied the SHAP method to evaluate the importance of various factors, used UMAP for dimensionality reduction, and employed the HDBSCAN clustering algorithm to classify the data, thereby enhancing the interpretability of the models. The results show that XGBoost outperforms RF in terms of accuracy, precision, recall, F1 score, KC, and MCC. The primary factors influencing landslide occurrence are topographic features, seismic activity, and precipitation intensity. This research not only introduces innovative machine learning techniques and interpretability methods for landslide susceptibility analysis but also provides a scientific foundation for emergency response and post-disaster planning related to landslide risks following the earthquake in Barkam City.