Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/nhess-2017-439
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
02 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).
Data Assimilation with An Improved Particle Filter and Its Application in TRIGRS Landslide Model
Changhu Xue, Guigen Nie, Haiyang Li, and Jing Wang GNSS Research Center, Wuhan University, Wuhan, 430079, China
Abstract. Particle filter has become a popular algorithm in data assimilation for its capability to handle non-linear or non-Gaussian state-space models, while it still be seriously influenced by its disadvantages. In this work, the particle filter algorithm is improved, proposed two methods to overcome the particle degeneration and improve particles’ efficiency. In this algorithm particle-propagating and resample method are ameliorated. The new particle filter is applied to Lorenz-63 model, verified its feasibility and effectiveness using only 20 particles. The root mean square difference(RMSD) of estimations converge to stable when there are more than 20 particles. Finally, we choose a 10 * 10 grid slope model of TRIGRS and carry out an assimilation experiment. Results show that the estimations of states can effectively correct the running-offset of the model and the RMSD is convergent after 3 days assimilation.

Citation: Xue, C., Nie, G., Li, H., and Wang, J.: Data Assimilation with An Improved Particle Filter and Its Application in TRIGRS Landslide Model, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-439, in review, 2018.
Changhu Xue et al.
Changhu Xue et al.

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Short summary
Landslide is a common and sudden geological disaster, which brings a lot of loss of life and property to the people. However, its monitoring and prevention is a difficult problem. This work introduced a new method which merges the observations into landslide evolutionary model. A nonlinear model and a simulation are applied to verified the algorithm. Results show that the processed data can improve the can improve the quality of prediction.
Landslide is a common and sudden geological disaster, which brings a lot of loss of life and...
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