Co-seismic landslides susceptibility evaluation of Bayesian random forest considering InSAR deformation: a case study of the Luding Ms6.8 earthquake

作者：Qiang Lin, Zhihua Zhang, Zhenghua Yang, Xinxiu Zhang, Xing Rong, Shuwen Yang, Hao Yuan, Xinyu Zhu, Wei Wang · 发表于：Geomatics Natural Hazards and Risk · 年份：2024 · DOI：10.1080/19475705.2024.2383783 · 被引用次数：12 · 研究领域：Landslides and related hazards、Synthetic Aperture Radar (SAR) Applications and Techniques、Cryospheric studies and observations

Strong earthquakes can frequently trigger a large number of co-seismic landslides. Obtaining an accurate susceptibility map of co-seismic landslides is crucial for post-disaster rescue and reconstruction efforts. In this study, the pre-seismic average annual surface deformation rate of the study area was obtained using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. Furthermore, the landslide hazards prior to the earthquake in multiple locations within the study area were analyzed. Subsequently, the deformation data was combined with 11 evaluation factors, including the distance to the fault and the Peak Ground Acceleration (PGA), to model the susceptibility of landslides. We constructed four models to evaluate the susceptibility of landslides in the study area: the Bayesian optimization random forest (BO-RF) model, the random forest model (RF), the logistic regression model (LR), and the support vector machine model (SVM). The BO-RF model outperformed the other models, achieving an AUC value of 0.984, an accuracy of 0.952, a precision of 0.953, a recall of 0.952, and an F1 score of 0.953. Furthermore, incorporating pre-seismic deformation features in the evaluation of co-seismic landslide susceptibility can effectively improve the reliability of model predictions, as compared to the evaluation model results without incorporating deformation factors. The obtained research results provide valuable data support for the rescue and manageme...