**7. Conclusions**

A hybrid model named GeoDIV was applied to produce a reliable LS map for the vicinity of Pinios artificial lake (Ilia, Greece). Based on the analysis of landslide and factor conditioning data, the GeoDIV framework exploited the multivariate GeoDetector to eliminate redundant factors and objectively quantify the individual and interactive impacts of the remaining ones (factor-level weights) on landslide occurrence. The bivariate IV was used for objectively quantifying the impacts of their classes (class-level weights). In practice, the integration of these two models increased their efficiency. The findings confirmed that hybrid modeling outperform modeling: the GeoDIV model yielded better results than the individual IV model in terms of accuracy and prediction ability. Thus, GeoDIV can be considered as a promising and robust model which can be beneficial not only to the current study area, but also to other regions with similar or even different conditions and settings.

In general, it was revealed that hybrid LS modeling assisted by multiple geospatial tools (RS and GIS) can contribute well to the production of reliable maps. The LS map produced by the GeoDIV model could be important basis for the regional or local authorities in order to develop both general (long-term) and emergency (short-term) strategies centered on "space design" disaster management. Knowledge about the potential for landslides in a region is valuable for policy makers, as it can allow them to select safe locations while planning land use and approving construction projects. Policy makers could also identify threatened settlements and roads, and in response take drastic disaster managemen<sup>t</sup> measures (including building engineered structures, planning evacuation routes and issuing early warnings).

Future research work will focus on testing the proposed hybrid modeling for LS assessments of other regions characterized by different environmental and/or human settings, with various landslide densities. Comparisons with other advanced models, such as machine learning models, will be also performed.

**Author Contributions:** Conceptualization, C.P.; methodology, C.P.; software, C.P.; validation, C.P.; formal analysis, C.P., M.G.G. and A.V.A.; data curation, C.P. and M.G.G.; visualization, C.P. and A.V.A.; writing—original draft preparation, C.P., M.G.G., A.V.A., N.P. and D.D.A.; writing—review and editing, C.P., M.G.G., A.V.A., N.P. and D.D.A.; supervision, N.P. and D.D.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors extremely appreciate the significant contributions of the journal's editor and reviewers to the handling and revision of this article.

**Conflicts of Interest:** The authors declare no conflict of interest.
