Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 24
Articles | Volume 18, issue 24
https://doi.org/10.5194/gmd-18-10185-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-10185-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 24
Methods for assessment of models
 | 
19 Dec 2025
Methods for assessment of models |  | 19 Dec 2025

Estimation of local training data point densities to support the assessment of spatial prediction uncertainty

Fabian Lukas Schumacher, Christian Knoth, Marvin Ludwig, and Hanna Meyer

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Short summary
Machine learning is increasingly used in environmental sciences for spatial predictions, but its effectiveness is challenged when models are applied beyond the areas they were trained on. We propose a Local Training Data Point Density (LPD) approach that considers how well a model's environment is represented by training data. This method provides a valuable tool for evaluating model applicability and uncertainties, crucial for broader scientific and practical applications.
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