Preprints
https://doi.org/10.5194/egusphere-2024-1779
https://doi.org/10.5194/egusphere-2024-1779
07 Oct 2024
 | 07 Oct 2024
Status: this preprint is open for discussion.

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

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

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

Status: open (until 06 Dec 2024)

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

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Short summary
We applied the Maximum Entropy model to characterize multi-hazard scenarios in karst environments, 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.