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Articles | Volume 12, issue 1
Articles | Volume 12, issue 1
https://doi.org/10.5194/soil-12-321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-12-321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 1
Original research article
 | 
30 Mar 2026
Original research article |  | 30 Mar 2026

Assessing the potential of complex artificial neural networks for modelling small-scale soil erosion by water

Nils Barthel, Simone Ott, Benjamin Burkhard, and Bastian Steinhoff-Knopp

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Short summary

This study compares neural networks and a random forest model for predicting soil erosion in agricultural cropland using long-term data from northern Germany. All models captured general erosion patterns, while more complex neural networks slightly improved the distinction between soil loss classes. A permutation importance analysis identified slope and machine direction vs. aspect as the most influential predictors across all models.

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