Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 6
Articles | Volume 26, issue 6
https://doi.org/10.5194/nhess-26-2743-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-2743-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 6
Research article
 | 
11 Jun 2026
Research article |  | 11 Jun 2026

Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)

Hedieh Soltanpour, Kamal Serrhini, Joel C. Gill, Sven Fuchs, and Solmaz Mohadjer

Viewed

Total article views: 8,727 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,842 2,026 859 8,727 263 340
  • HTML: 5,842
  • PDF: 2,026
  • XML: 859
  • Total: 8,727
  • BibTeX: 263
  • EndNote: 340
Views and downloads (calculated since 07 Oct 2024)
Cumulative views and downloads (calculated since 07 Oct 2024)

Viewed (geographical distribution)

Total article views: 8,727 (including HTML, PDF, and XML) Thereof 8,694 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 11 Jun 2026
Download
Short summary
We applied the Maximum Entropy model to characterise multi-hazard scenarios in a karst environment, focusing on flood-triggered sinkholes in Val d'Orléans, France. Karst terrains as multi-hazard forming areas, have received little attention in multi-hazard literature. Our study developed a multi-hazard susceptibility map to forecast the spatial distribution of these hazards. The findings improve understanding of hazard interactions and demonstrate the model's utility in multi-hazard analysis.
Read more
Share
Altmetrics
Final-revised paper
Preprint