A Study of Small-scale Landslides Susceptibility Dynamic Assessment in South Chian: Merging SBAS-InSAR and Machine Learning

Abstract. Shallow landslides, triggered by factors such as heavy rainfall and human engineering activities, are characterized by their sudden occurrence, wide distribution, and small scale, which pose significant threats to human life, especially in southern China. Landslide susceptibility assessment (LSA) is crucial for disaster prevention, mitigation, and land-use planning. Traditional assessment methods, such as field surveys and statistical models, predominantly depend on static geological environment factors, which exhibit inherent limitations in capturing spatiotemporal information on dynamic surface deformation. This deficiency directly leads to a lack of timeliness in hazard assessment, which is particularly important for sudden disasters. In this study, small Baseline Subset (SBAS – InSAR) and machine learning were married to explore a dynamic method in LSA, and the Conghua district in Guangzhou was selected as the study area. First, a dataset consisting of 326 historical landslides and 10 static factors, and 2 dynamic factors in the area was prepared. Then, the dataset was divided into two parts, one for modeling training (80 %) and the other for testing (20 %). Third, factors and samples were involved in the modeling of Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). Finally, the performance of these models was validated through the area under the curve (AUC) and compared with the models analyzed by 10 static factors only. The results show that the XGBoost model exhibits the best performance, with an AUC of 0.933, and the models considered dynamic factors all performed better than that of static factors only. This study demonstrates that merging SBAS-InSAR and machine learning can provide a reliable technical approach for dynamic assessment of small-scale LSA, which is of great significance for targeted disaster prevention and mitigation in rural and hilly areas of South China.