Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)
4 affiliations
- 1UMR 7324 CITERES, University of Tours, Tours, France
- 2School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK
- 3BOKU University, Department of Civil Engineering and Natural Hazards, Vienna, Austria
- 4University of Tübingen, Tübingen, Germany
Abstract
In this study, we extend the application of the Maximum Entropy model (MaxEnt), traditionally applied to ecological research and less explored in natural hazard studies, to a novel context by characterising a multi-hazard scenario (i.e., flood-triggered sinkholes) in the Orléans karst region (Val d'Orléans) of France. Many regions of the world exhibit complex hazard landscapes where networks of multi-hazard interrelationships (cascades) pose challenges due to the potential interactions between hazards and the different temporal and spatial scales of hazard events. While mountainous, coastal and volcanic regions have been recognised as multi-hazard forming zones, karst terrains have received little attention despite being prone to multi-hazard events due to their distinct geology, geomorphology, hydrogeology and other environmental characteristics. Incorporating karst-specific multi-hazard scenarios supports disaster risk reduction efforts by raising the awareness of citizens, protecting elements at risk and facilitating decisions on disaster prevention. To support this aim, we developed a multi-hazard susceptibility map for the karst region of Val d'Orléans that characterises flood-triggered sinkholes. We applied MaxEnt, a machine learning method, to forecast the spatial probability distribution of flood-triggered sinkholes. Model inputs included the location of past sinkhole occurrences and geo-environmental factors contributing to sinkhole formation (e.g., topography, local geology, hydrology and flood hazard). We validated the performance of the model by initially using 70 % of the sinkhole inventory data and keeping the remaining 30 % for testing. This validation process assessed the model's performance using the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC). The resulting map reveals areas located up to 1 km south of the Loire River and areas with lowest elevation with highest susceptibility to flood-triggered sinkholes. We conclude that our approach to producing this type of multi-hazard scenario and map is useful for identifying flood-triggered sinkholes in Val d'Orléans and other karst areas around the globe, supporting effective land use planning.
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BibTeX
@article{soltanpour2026multihazard,
title = {{Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)}},
author = {Hedieh Soltanpour and Kamal Serrhini and Joel C. Gill and Sven Fuchs and Solmaz Mohadjer},
journal = {Natural hazards and earth system sciences},
year = {2026},
month = jun,
doi = {10.5194/nhess-26-2743-2026},
url = {https://doi.org/10.5194/nhess-26-2743-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)
AU - Hedieh Soltanpour
AU - Kamal Serrhini
AU - Joel C. Gill
AU - Sven Fuchs
AU - Solmaz Mohadjer
PY - 2026
DA - 2026/06/11
JO - Natural hazards and earth system sciences
DO - 10.5194/nhess-26-2743-2026
UR - https://doi.org/10.5194/nhess-26-2743-2026
AB - In this study, we extend the application of the Maximum Entropy model (MaxEnt), traditionally applied to ecological research and less explored in natural hazard studies, to a novel context by characterising a multi-hazard scenario (i.e., flood-triggered sinkholes) in the Orléans karst region (Val d'Orléans) of France. Many regions of the world exhibit complex hazard landscapes where networks of multi-hazard interrelationships (cascades) pose challenges due to the potential interactions between hazards and the different temporal and spatial scales of hazard events. While mountainous, coastal and volcanic regions have been recognised as multi-hazard forming zones, karst terrains have received little attention despite being prone to multi-hazard events due to their distinct geology, geomorphology, hydrogeology and other environmental characteristics. Incorporating karst-specific multi-hazard scenarios supports disaster risk reduction efforts by raising the awareness of citizens, protecting elements at risk and facilitating decisions on disaster prevention. To support this aim, we developed a multi-hazard susceptibility map for the karst region of Val d'Orléans that characterises flood-triggered sinkholes. We applied MaxEnt, a machine learning method, to forecast the spatial probability distribution of flood-triggered sinkholes. Model inputs included the location of past sinkhole occurrences and geo-environmental factors contributing to sinkhole formation (e.g., topography, local geology, hydrology and flood hazard). We validated the performance of the model by initially using 70 % of the sinkhole inventory data and keeping the remaining 30 % for testing. This validation process assessed the model's performance using the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC). The resulting map reveals areas located up to 1 km south of the Loire River and areas with lowest elevation with highest susceptibility to flood-triggered sinkholes. We conclude that our approach to producing this type of multi-hazard scenario and map is useful for identifying flood-triggered sinkholes in Val d'Orléans and other karst areas around the globe, supporting effective land use planning.
SN - 1561-8633
SN - 1684-9981
ER -