| 07 Oct 2024
Multi-hazard susceptibility mapping in the karst context using a machine-learning method (MaxEnt)
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.
Competing interests: SF and SM are members of the editorial board of Natural Hazards and Earth System Sciences.
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Reviewer's comments:
The paper presents the methods adopted and implemented for machine learning of susceptibility mapping in a two component multi-hazard complex, using MaxExtent and on this basis I would suggest that the Scientific significance is Good to excellent - 2-1; the Scientific Quality is good, methods are clearly explained and reproducible, with the exception that the groundwater levels cannot be related to the ground levels (see Fig. 6 e depth to water on the fig and stated as groundwater level in caption, and datum for ground levels is not presented), which may also impact the interpretation and results, therefore my ranking would be Good to Fair 2-3; The Presentation Quality is Good (2) albeit two styles of writing are identifiable in the text, but both are readily interpreted by the reader.
With respect to the presentation there are numerous cases of differences between cited reference dates and those reported in the reference list; not all references cited, e.g. Kim and Nam 2018, Pazzi et al., 2018; line 509 Radosavljevic and Anderson rather than et al.; are in the reference list;
some references need correcting, e.g. Mokhtari or Mokhrarai; .
There are some minor typos/ spelling issues, e.g. line 45 partially = partly, line 53 multi-hazard forming zones = zones that host multi-hazards? Line 80 forming landscape = landscape context? Line 174 the exposure of groundwater to rather than and easily eroded ...; line 178 bottom = base; line 232 Quaternary Alluvium; line 238 could be improved to ... which were mainly attributed to karst collapses ....; line 240 happened = occurred; line 250 for = to; Methods section - should this be written in the past tense? Should the title for section 2 be edited to The multi-hazard environment of karst terrains? Line 591 should flattens out to be falls to? Line 607 should this read with increased thickness of alluvial deposits? Figure 8 e again groundwater conditions need to be made clearer. Line 684 ... means of a classification ...Line 717 groundwater conditions need to be defined more clearly.
The content is relevant for NHESS and its international audience, and the title clearly reflects content albeit the word "the" might sensibly be changed to "a" as karst contexts can be very different. The abstract provides a full and clear overview of the content. Line 23 appears to be missing some words e.g. .... scenarios "in resilience procedures" ... or similar.
The references are relevant and acknowledge prior research. The length is appropriate with a strong discussion and conclusions.
I hope that this helps to progress the paper through the publication process and apologise for the delay and connectivity issues.