Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 9
Articles | Volume 26, issue 9
https://doi.org/10.5194/nhess-26-4457-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-4457-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 9
Research article
 | 
17 Sep 2026
Research article |  | 17 Sep 2026

Bayesian forecasting of triggered landslides

Flavia Ferriero, Fausto Guzzetti, and Warner Marzocchi

Viewed

Total article views: 1,259 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
876 294 89 1,259 85 52 63
  • HTML: 876
  • PDF: 294
  • XML: 89
  • Total: 1,259
  • Supplement: 85
  • BibTeX: 52
  • EndNote: 63
Views and downloads (calculated since 02 Apr 2026)
Cumulative views and downloads (calculated since 02 Apr 2026)

Viewed (geographical distribution)

Total article views: 1,259 (including HTML, PDF, and XML) Thereof 1,235 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Oct 2026
Download
Short summary
Landslides cause thousands of deaths and billions in damages yearly, yet predicting them remains a major challenge. We developed a Bayesian method that estimates landslide probability as a function of rainfall, explicitly accounting for uncertainty. Applied in southern Italy, landslide probability increases gradually with rainfall, with no sharp thresholds in the triggering conditions. This approach supports a more uncertainty-aware landslide risk management.
Read more
Share
Altmetrics
Final-revised paper
Preprint