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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2025
Research article |
| 05 Feb 2025
| 05 Feb 2025
Predicting the thickness of shallow landslides in Switzerland using machine learning
Christoph Schaller
CORRESPONDING AUTHOR
Bern University of Applied Sciences – HAFL, Länggasse 85, 3052 Zollikofen, Switzerland
University of Amsterdam UVA – IBED, Science Park 904, 1098 XH Amsterdam, the Netherlands
Luuk Dorren
Bern University of Applied Sciences – HAFL, Länggasse 85, 3052 Zollikofen, Switzerland
Massimiliano Schwarz
Bern University of Applied Sciences – HAFL, Länggasse 85, 3052 Zollikofen, Switzerland
Christine Moos
Bern University of Applied Sciences – HAFL, Länggasse 85, 3052 Zollikofen, Switzerland
Arie C. Seijmonsbergen
University of Amsterdam UVA – IBED, Science Park 904, 1098 XH Amsterdam, the Netherlands
E. Emiel van Loon
University of Amsterdam UVA – IBED, Science Park 904, 1098 XH Amsterdam, the Netherlands
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The simulation of runout zones is crucial for mitigating gravitational natural hazards. To address the trade off between accuracy and simplicity, equations of motion for the energy line principle are implemented and applied in a case study. The results show that this approach improves simulation accuracy while maintaining model simplicity. This method offers a promising solution for preliminary analyses of gravitational natural hazards at large scales.
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Peatlands are vital terrestrial ecosystems that can serve as archives, preserving records of past vegetation and climate. We reconstructed the vegetation history over the last 2600 years of the Beerberg peatland and surrounding area in the Thuringian Forest in Germany using multiple analyses. We found that, although the forest composition transitioned and human influence increased, the peatland remained relatively stable until more recent times, when drainage and dust deposition had an impact.
Feiko Bernard van Zadelhoff, Adel Albaba, Denis Cohen, Chris Phillips, Bettina Schaefli, Luuk Dorren, and Massimiliano Schwarz
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Shallow landslides pose a risk to people, property and infrastructure. Assessment of this hazard and the impact of protective measures can reduce losses. We developed a model (SlideforMAP) that can assess the shallow-landslide risk on a regional scale for specific rainfall events. Trees are an effective and cheap protective measure on a regional scale. Our model can assess their hazard reduction down to the individual tree level.
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Publication in NHESS not foreseen
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In the daily practice of rockfall hazard analysis, trajectory simulations are used to delimit runout zones. To do so, the expert needs to separate "realistic" from "unrealistic" simulated groups of trajectories. This is often done on the basis of reach probability values. This paper provides a basis for choosing a reach probability threshold value for delimiting the rockfall runout zone, based on recordings and simulations of recent rockfall events at 18 active rockfall sites in Europe.
Carrie L. Thomas, Boris Jansen, E. Emiel van Loon, and Guido L. B. Wiesenberg
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Plant organs, such as leaves, contain a variety of chemicals that are eventually deposited into soil and can be useful for studying organic carbon cycling. We performed a systematic review of available data of one type of plant-derived chemical, n-alkanes, to determine patterns of degradation or preservation from the source plant to the soil. We found that while there was degradation in the amount of n-alkanes from plant to soil, some aspects of the chemical signature were preserved.
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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.
We developed a machine-learning-based approach to predict the potential thickness of shallow...
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