Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide

Abstract

Due to the complex and non-linear characteristics of landslide evolution, the deformation law of landslides is always hard to be predicted. Considering the dynamical evolution process of landslide physical mechanism, a Physics-Informed Data Assimilation (PIDA) method is first proposed for landslide displacement forecasting. It can enhance the prediction ability of the physical forecasting model, and solves the problem of ignoring the physical significance of landslides in mathematical prediction model. Additionally, the superiority of Particle Filter (PF) in nonlinear and non-Gaussian system determines its application in complex problem. Aiming at the hydrodynamic pressure-driven landslide, the Sliding Zone Deterioration (SZD) model is constructed as the state equation of the PF algorithm. And the observation equation is formed by the relationship between landslide deformation and monitoring datasets. The physics-informed landslide displacement prediction model serves as a bridge, consisting of the shear strength parameter dynamic updated model and physical mechanism analysis model, for key parameters feedback, modification, and update. Comparing the results to traditional Long Short-Term Memory (LSTM) and Back Propagation Neural Network (BPNN) methods, the PIDA method combining triggering and instability mechanisms performs better in accuracy. Moreover, it enables dynamic and reliable landslide deformation prediction.