National-Scale Landslide Susceptibility Mapping inAustria Using Fuzzy Best-WorstMulti-Criteria Decision-Making

Landslides are one of the most detrimental geological disasters that intimidate human livesalong with severe damages to infrastructures and they mostly occur in the mountainous regionsacross the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potentialareas that are prone to landslides and could have an impact on decreasing the possible damages.The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was appliedfor LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSMwas investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For thisstudy, a wide range of data was sourced from the Geological Survey of Austria, the Austrian LandInformation System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected.We used nine conditioning factors that were based on the previous studies and geomorphologicalcharacteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distanceto drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slopeconditioning factor was chosen as the best criterion (highest impact on LSM) and the distance toroads was considered as the worst criterion (lowest impact on LSM). LSM was generated for theregion based on the best and worst criterion. The findings show the robustness of FBWM in landslidesusceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM canobtain higher accuracy as compared to FAHP. The finding of this research can help authorities anddecision-makers to provide effective strategies and plans for landslide prevention and mitigation atthe national level.