Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 5
Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-2187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 5
Model description paper
 | Highlight paper
 | 
15 Mar 2024
Model description paper | Highlight paper |  | 15 Mar 2024

Minimum-variance-based outlier detection method using forward-search model error in geodetic networks

Utkan M. Durdağ

Viewed

Total article views: 1,615 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,258 254 103 1,615 63 66
  • HTML: 1,258
  • PDF: 254
  • XML: 103
  • Total: 1,615
  • BibTeX: 63
  • EndNote: 66
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,615 (including HTML, PDF, and XML) Thereof 1,605 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
Executive editor
Robust outlier detection is a challenge for all areas of science that deal with real data. Here, the author describes a new approach to this in the field of geodesy, but does so in a readable and accessible way. It will therefore be valuable reading for those beyond that field.
Read more
Short summary
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
Read more
Share