Articles | Volume 37, issue 1
https://doi.org/10.5194/angeo-37-77-2019
https://doi.org/10.5194/angeo-37-77-2019
Regular paper
 | 
31 Jan 2019
Regular paper |  | 31 Jan 2019

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Mingyu Kim and Jeongrae Kim

Viewed

Total article views: 3,053 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,396 603 54 3,053 50 50
  • HTML: 2,396
  • PDF: 603
  • XML: 54
  • Total: 3,053
  • BibTeX: 50
  • EndNote: 50
Views and downloads (calculated since 25 Sep 2018)
Cumulative views and downloads (calculated since 25 Sep 2018)

Viewed (geographical distribution)

Total article views: 3,053 (including HTML, PDF, and XML) Thereof 2,546 with geography defined and 507 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Mar 2024
Download
Short summary
Spatial extrapolation of an ionosphere TEC map was carried out using a SVM learning algorithm. There has been much research on the temporal extrapolation or prediction of TEC time series, but the spatial extrapolation has rarely been attempted. Some researchers have performed simultaneous extrapolation both in time and in spatial domains, but this research covers the spatial extrapolation only by using an inner TEC map. This spatial TEC extrapolation can be useful for small countries.