Articles | Volume 26, issue 6
https://doi.org/10.5194/nhess-26-2743-2026
https://doi.org/10.5194/nhess-26-2743-2026
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: 9,060 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,079 2,104 877 9,060 281 357
  • HTML: 6,079
  • PDF: 2,104
  • XML: 877
  • Total: 9,060
  • BibTeX: 281
  • EndNote: 357
Views and downloads (calculated since 07 Oct 2024)
Cumulative views and downloads (calculated since 07 Oct 2024)

Viewed (geographical distribution)

Total article views: 9,060 (including HTML, PDF, and XML) Thereof 8,948 with geography defined and 112 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 13 Aug 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.
Share
Altmetrics
Final-revised paper
Preprint