Accurately predicting landslide displacement remains challenging due to complex movement mechanisms and large uncertainties. Here, we develop a physics-informed data augmentation framework to tackle this challenge. By incorporating external drivers, including rainfall and reservoir water level, we establish quantitative relationships between key aging parameters (e.g., creep and strength parameters) and target indicators (e.g., responses of monitoring point (RMS)) using surrogate models. We use time-series monitoring data to sequentially update the probability distribution of aging parameters. The posterior distributions are used to adaptively and dynamically predict landslide displacement. We find that the temporal evolution of creep parameters closely resembles the observed displacement, exhibiting both a periodic pattern and a long-term growth trend, while the strength parameters show a consistent deterioration over time. These findings offer a mechanistic interpretation of the step-like deformation behavior. The integration of movement mechanisms and observed data not only reveals the evolutionary progress of aging parameters but also improves the reliability of prediction by effectively reducing uncertainties.
Southwest Jiaotong University, China (SWJTU) International Consortium on Geo-disaster Reduction (ICGdR) UNESCO Chair on Geoenvironmental Disaster Reduction
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Southwest Jiaotong University, China (SWJTU) International Consortium on Geo-disaster Reduction (ICGdR) UNESCO Chair on Geoenvironmental Disaster Reduction