Articles | Volume 18, issue 10
https://doi.org/10.5194/nhess-18-2801-2018
https://doi.org/10.5194/nhess-18-2801-2018
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 Xue, Guigen Nie, Haiyang Li, and Jing Wang

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (24 Apr 2018) by Thomas Glade
AR by Guigen Nie on behalf of the Authors (09 May 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (08 Jul 2018) by Thomas Glade
RR by Anonymous Referee #2 (18 Jul 2018)
RR by Anonymous Referee #1 (21 Jul 2018)
ED: Publish subject to minor revisions (review by editor) (10 Aug 2018) by Thomas Glade
AR by Guigen Nie on behalf of the Authors (18 Aug 2018)  Author's response
ED: Publish as is (14 Oct 2018) by Thomas Glade
AR by Guigen Nie on behalf of the Authors (15 Oct 2018)  Author's response    Manuscript
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Short summary
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.
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