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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
07 Aug 2024
Methods for assessment of models |
| 07 Aug 2024
| 07 Aug 2024
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Jan Linnenbrink
CORRESPONDING AUTHOR
Institute of Landscape Ecology, University of Münster, Münster, Germany
Carles Milà
Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
Universitat Pompeu Fabra (UPF), Barcelona, Spain
Marvin Ludwig
Institute of Landscape Ecology, University of Münster, Münster, Germany
Hanna Meyer
Institute of Landscape Ecology, University of Münster, Münster, Germany
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- Global hotspots of mycorrhizal fungal richness are poorly protected M. Van Nuland et al. 10.1038/s41586-025-09277-4
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- An interpretable spatially weighted machine learning approach for revealing spatial nonstationarity impacts of the built environment on air pollution S. Wang et al. 10.1016/j.buildenv.2025.113150
- On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction Y. Wang et al. 10.1080/15481603.2025.2460513
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- Predicting historical oxygen deficiency areas in the western Baltic Sea: A multi-model approach S. Piehl et al. 10.1016/j.marpolbul.2025.118464
- Interpretable machine learning for academic performance prediction: A SHAP-based analysis of key influencing factors Y. Guan et al. 10.1080/14703297.2025.2532050
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6 citations as recorded by crossref.
- kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation J. Linnenbrink et al. 10.5194/gmd-17-5897-2024
- Soil carbon in the world’s tidal marshes T. Maxwell et al. 10.1038/s41467-024-54572-9
- Random forests with spatial proxies for environmental modelling: opportunities and pitfalls C. Milà et al. 10.5194/gmd-17-6007-2024
- High-resolution canopy fuel maps based on GEDI: a foundation for wildfire modeling in Germany J. Heisig et al. 10.1088/2752-664X/adaaf9
- Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion F. Aramburu-Merlos et al. 10.1038/s41467-024-48859-0
- A review of regularised estimation methods and cross-validation in spatiotemporal statistics P. Otto et al. 10.1214/24-SS150
Latest update: 28 Aug 2025
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.
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but...
