Articles | Volume 12, issue 1
https://doi.org/10.5194/soil-12-321-2026
https://doi.org/10.5194/soil-12-321-2026
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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Cited articles

Alewell, C., Borrelli, P., Meusburger, K., and Panagos, P.: Using the USLE: Chances, challenges and limitations of soil erosion modelling, International Soil and Water Conservation Research, 7, 203–225, https://doi.org/10.1016/j.iswcr.2019.05.004, 2019. a
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, https://doi.org/10.1093/bioinformatics/btq134, 2010. a
Anache, J. A., Flanagan, D. C., Srivastava, A., and Wendland, E. C.: Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado, Sci. Total Environ., 622, 140–151, https://doi.org/10.1016/j.scitotenv.2017.11.257, 2018. a
Avand, M., Mohammadi, M., Mirchooli, F., Kavian, A., and Tiefenbacher, J. P.: A new approach for smart soil erosion modeling: integration of empirical and machine-learning models, Environ. Model. Assess., 28, 145–160, https://doi.org/10.1007/s10666-022-09858-x, 2023. a, b, c
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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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