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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 10 | Copyright
Nat. Hazards Earth Syst. Sci., 18, 2801-2807, 2018
https://doi.org/10.5194/nhess-18-2801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Oct 2018

Research article | 26 Oct 2018

Data assimilation with an improved particle filter and its application in the TRIGRS landslide model

Changhu Xue1, Guigen Nie1,2, Haiyang Li1, and Jing Wang1 Changhu Xue et al.
  • 1GNSS Research Center, Wuhan University, Wuhan, 430079, China
  • 2Collaborative Innovation Center for Geospatial Information Technology, Wuhan, 430206, China

Abstract. Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.

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Landslide is a common and sudden geological disaster, which is difficult to monitor and prevent efficiently. This paper introduces an improved algorithm of data assimilation that merges the observations into a landslide evolutionary model. A nonlinear model experiment is applied to verify the feasibility of the algorithm. An application of landslide simulation is carried out. Results show that the estimations of states can effectively correct the running offset after assimilation.
Landslide is a common and sudden geological disaster, which is difficult to monitor and prevent...
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