Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 15
Articles | Volume 17, issue 15
https://doi.org/10.5194/gmd-17-5897-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5897-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 15
Methods for assessment of models
 | 
07 Aug 2024
Methods for assessment of models |  | 07 Aug 2024

kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation

Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer

Viewed

Total article views: 3,159 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,426 640 93 3,159 109 161
  • HTML: 2,426
  • PDF: 640
  • XML: 93
  • Total: 3,159
  • BibTeX: 109
  • EndNote: 161
Views and downloads (calculated since 05 Jul 2023)
Cumulative views and downloads (calculated since 05 Jul 2023)

Viewed (geographical distribution)

Total article views: 3,159 (including HTML, PDF, and XML) Thereof 3,126 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2026
Download
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Read more
Share