Articles | Volume 25, issue 2
https://doi.org/10.5194/nhess-25-467-2025
https://doi.org/10.5194/nhess-25-467-2025
Research article
 | 
05 Feb 2025
Research article |  | 05 Feb 2025

Predicting the thickness of shallow landslides in Switzerland using machine learning

Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon

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Cited articles

Ali, A., Huang, J., Lyamin, A. V., Sloan, S. W., Griffiths, D. V., Cassidy, M. J., and Li, J. H.: Simplified quantitative risk assessment of rainfall-induced landslides modelled by infinite slopes, Eng. Geol., 179, 102–116, https://doi.org/10.1016/j.enggeo.2014.06.024, 2014. a
Arnold, P. and Dorren, L.: The Importance of Rockfall and Landslide Risks on Swiss National Roads, in: Engineering Geology for Society and Territory – Volume 6, edited by: Lollino, G., Giordan, D., Thuro, K., Carranza-Torres, C., Wu, F., Marinos, P., and Delgado, C., Springer International Publishing, Cham, 671–675, ISBN 978-3-319-09060-3, https://doi.org/10.1007/978-3-319-09060-3_120, 2015. a
Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747–2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016. a
BAFU: Topographische Einzugsgebiete Schweizer Gewässer Schweiz, Ausgabe 2019, https://data.geo.admin.ch/ch.bafu.wasser-teileinzugsgebiete_2/ (last access: 23 January 2025), 2019. a, b
BAFU: Produktionsregionen LFI, https://data.geo.admin.ch/ch.bafu.landesforstinventar-produktionsregionen/, 2020. a
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We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
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