Abstract
Abstract. 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.
Highlights
Landslide is a common geological hazard which greatly endangers the security of property and lives of the people (Huang et al, 2017; Froude and Petley, 2018; Zhang and Huang, 2018; Pham et al, 2018)
This paper presents a practical strategy for accurately predicting landslide displacement by coupling landslide deformation with external factors
The particle filter (PF) data assimilation algorithm was integrated with the simultaneous state and parameter estimation (SSPE) method
Summary
Landslide is a common geological hazard which greatly endangers the security of property and lives of the people (Huang et al, 2017; Froude and Petley, 2018; Zhang and Huang, 2018; Pham et al, 2018). The above methods have certain practicability in the prediction of landslides, it is still problematic to carry out forecasts of rainfall-induced landslides in real time (Yin et al, 2010) – for the reason that surveillance photographs or optical remote-sensing satellites are not immediately available (Lee et al, 2019). It may take days, even months, to obtain field data and establish a process model of the study area. By combining surface observational data with the process model, data assimilation provides an optimal true value that is continu-
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
