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: 2,496 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,925 493 78 2,496 92 118
  • HTML: 1,925
  • PDF: 493
  • XML: 78
  • Total: 2,496
  • BibTeX: 92
  • EndNote: 118
Views and downloads (calculated since 05 Jul 2023)
Cumulative views and downloads (calculated since 05 Jul 2023)

Viewed (geographical distribution)

Total article views: 2,496 (including HTML, PDF, and XML) Thereof 2,491 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Nov 2025
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