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

Viewed

Total article views: 2,336 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,524 730 82 2,336 226 69 72
  • HTML: 1,524
  • PDF: 730
  • XML: 82
  • Total: 2,336
  • Supplement: 226
  • BibTeX: 69
  • EndNote: 72
Views and downloads (calculated since 02 Jan 2018)
Cumulative views and downloads (calculated since 02 Jan 2018)

Viewed (geographical distribution)

Total article views: 2,336 (including HTML, PDF, and XML) Thereof 2,149 with geography defined and 187 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
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.
Altmetrics
Final-revised paper
Preprint