Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 2
Articles | Volume 25, issue 2
https://doi.org/10.5194/nhess-25-467-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-467-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 2
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

Viewed

Total article views: 3,724 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,880 644 200 3,724 90 140
  • HTML: 2,880
  • PDF: 644
  • XML: 200
  • Total: 3,724
  • BibTeX: 90
  • EndNote: 140
Views and downloads (calculated since 22 May 2024)
Cumulative views and downloads (calculated since 22 May 2024)

Viewed (geographical distribution)

Total article views: 3,724 (including HTML, PDF, and XML) Thereof 3,527 with geography defined and 197 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Feb 2026
Download
Short summary
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint