Abstract
Our objective was a comparison between Random Forest (RF, machine learning) and Generalized Additive Mo-del (GAM, conventional method) for landslide susceptibility modeling of rainfall-triggered landslides.
In the Styrian Basin more than 3,000 landslides occurred after heavy rainfall events in 2009 and 2014 causing significant da-mage to human infrastructure (Knevels et al., 2020).
The area affected during a 2009-type event could grow by up to 45% in a 4 K global warming scenario (Maraun et al., 2022; Knevels et al., 2023), making appropriate and robust landslide susceptibility predictions a necessary prerequisite for decision-makers.
In the Styrian Basin more than 3,000 landslides occurred after heavy rainfall events in 2009 and 2014 causing significant da-mage to human infrastructure (Knevels et al., 2020).
The area affected during a 2009-type event could grow by up to 45% in a 4 K global warming scenario (Maraun et al., 2022; Knevels et al., 2023), making appropriate and robust landslide susceptibility predictions a necessary prerequisite for decision-makers.
| Original language | English |
|---|---|
| Publication status | Published - 14 Nov 2023 |
| Event | World Landslide Forum 6: Landslide Science for Sustainable Development - Palazzo degli Affari & Palazzo dei Congressi, Florence, Italy Duration: 14 Nov 2023 → 17 Nov 2023 Conference number: 6 https://wlf6.org/ |
Conference
| Conference | World Landslide Forum 6 |
|---|---|
| Abbreviated title | WLF6 |
| Country/Territory | Italy |
| City | Florence |
| Period | 14/11/23 → 17/11/23 |
| Internet address |
Research Field
- Road Infrastructure Assessment, Modelling and Safety Evaluation
