EGU26-17324, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-17324
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Friday, 08 May, 10:45–12:30 (CEST), Display time Friday, 08 May, 08:30–12:30
 
Hall X2, X2.113
Machine learning and rainfall threshold-based assessment of landslide hazards in Vietnam
Tran Trung Hieu1,2,3, Łukasz Pawlik2, Pham Van Tien3, and Nguyen Cong Quan3
Tran Trung Hieu et al.
  • 1International Environmental Doctoral School, University of Silesia in Katowice, Sosnowiec, Poland
  • 2Institute of Earth Sciences, University of Silesia in Katowice, Sosnowiec, Poland
  • 3Institute of Earth Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam

For an effective landslide hazard assessment, it is essential to accurately predict the occurrence, timing, and magnitude of landslides. This work presents a detailed analysis of landslide spatiotemporal probability and size distribution for a case study Vietnam. Spatial probability was modeled using Extreme Gradient Boosting (XGB), Random Forest (RF), and Logistic Regression (LR) with 12 predictor variables and a landslide inventory recorded from 2017 to 2024. Temporal probability was estimated using daily rainfall data, applying an event rainfall–duration threshold in combination with a Poisson model. Landslide size probabilities were derived from a probability density function (PDF). Finally, a set of hazard maps was produced for three different time periods and three landslide size classes.

The study has been supported by the Polish National Science Centre (project no 2023/49/B/ST10/02879).

How to cite: Trung Hieu, T., Pawlik, Ł., Van Tien, P., and Cong Quan, N.: Machine learning and rainfall threshold-based assessment of landslide hazards in Vietnam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17324, https://doi.org/10.5194/egusphere-egu26-17324, 2026.