Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

Wang, Jing; Nie, Guigen; Gao, Shengjun; Xue, Changhu

Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, and applying a classical prediction method will result in significant error. The data assimilation method offers a new way to merge multisource data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide systems. In this paper, simultaneous state and parameter estimation (SSPE) using particle-filter-based data assimilation is applied to predict displacement of the landslide. A landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan Province, China, as the experiment site to test the SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of the SSPE assimilation strategy in short-term landslide displacement estimation.

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Wang, Jing / Nie, Guigen / Gao, Shengjun / et al: Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation. 2019. Copernicus Publications.

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