Particle Filter Data Assimilation Method for Loess Landslide Risk Assessment Combined with TRIGRS Model

Abstract

Landslide disasters pose a grave threat to national infrastructure projects, making landslide disaster risk assessment in engineering areas crucial for ensuring the safety of railway operations. Typically, such assessments involve employing frequency analysis, probability analysis, and deterministic analysis methods. Among these methods, the establishment of a physical deterministic model based on the mechanisms of rainfall infiltration and water accumulation yields more objective evaluation results and exhibits excellent applicability. However, physically deterministic models often necessitate the inclusion of numerous geotechnical parameters in their calculations, leading to certain limitations. Factors like temporal and spatial variability, as well as the uncertainty in geotechnical parameters, influence these models. To enhance the prediction accuracy of landslide risk assessments, this study focuses on the Gaojiawan landslide as the research area. By utilizing the particle filter algorithm, the study assimilates safety factor (Fs) data from the TRIGRS model, incorporating SBAS-InSAR observation data. Additionally, it updates the internal friction angle parameter of the model. The results reveal a gradual decrease in the safety factor of the Gaojiawan landslide following assimilation. Moreover, the safety factor at the front edge of the slope is significantly lower than that at the rear edge, aligning more closely with current observations. Real- time updates of the internal friction angle parameters are achieved, gradually aligning the parameters with the observed values. As a result, the root mean square deviation of the model decreases from 0.17 to 0.04, bringing the model's prediction closer to the observed values. Consequently, landslide risk assessment based on the particle filter assimilation method more accurately reflects the current landslide situation and exhibits higher prediction accuracy.