## 5 Conclusions  
The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas for which landslide inventories are not generated due to financial or reachability constraints. Thus, this study evaluated whether landslide susceptibility could be effectively assessed in such areas where landslide inventory was available for an adjacent region. Having considered the results from the two landslide susceptibility maps generated by cross-modeling, we can assert that susceptibility zones generated based on inventory located in an adjacent region will be different for 53% of the analyzed area when compared with a susceptibility map generated using landslide inventory. On the contrary, it can be estimated that 47% of the map generated based on inventory for a surrounding area will be similar to a map generated in a traditional manner (using landslide inventory for the analyzed region). Based on a comparison of the accuracy measures for the training and validation areas, we can conclude that the cross-modeling of landslide susceptibility has great influence on the accuracy of landslide susceptibility determination. This is directly connected with sampling strategy (landslide selection for susceptibility modeling). In this study, we applied a sampling strategy completely different to that widely presented in the literature [13, 14, 20, 55]. Namely, we performed cross-modeling based on landslides located in the cluster (Lososina/Grodek region) defined by the specific natural boundary (Roznow Lake and Dunaje River). In the LSM literature, the landslide events used for modeling are usually randomly and evenly distributed across the study area. This allows capturing of the variety of geological conditions and the susceptibility to landslides. Therefore, in a perfect scenario, it is desirable to use landslides distributed evenly across the study area and located in various geological settings. This will introduce the effect of autocorrelation and allow the effective assessment of landslide susceptibility. Nevertheless, when such a landslide sampling strategy is not possible due to the lack of landslide inventory, landslide susceptibility can be assess based on landslide inventory available in adjacent region. However, discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2-3 susceptibility zone difference). Nonetheless, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be used. Still, the low reliability of such a map in areas of critical geological structures should be borne in mind. This applies to LSM in other regions where the geology can be much more complex. As such, the discrepancies among the LSMs generated based on the strategy presented in this study are particularly relevant given that the overestimation or underestimation of susceptibility can have crucial effects on land-use management and civil protection planning. The optimal map should be able to depict most potential landslide events and, at the same time, be effective and accurate for preventing failures in the study area. Therefore, specific landslide inventory is certainly needed to more reliably estimate landslide susceptibility in complex geological areas. Conceptualization, K.P.-F.; experiments, K.P.-F., N.O., M.P. ; data capture, K.P.-F.; formal analysis, K.P.-F., N.O., M.P. ; validation, K.P.-F.; writing--original draft preparation, K.P.-F.; writing--review and editing, K.P.-F.; supervision, K.P.-F.; funding acquisition, K.P.-F. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Science Centre of Poland (Grant No. 2018/28/T/ST10/00528). The authors are very grateful to [PERSON] from Polish National Geological Institute for providing geological data and documentation from the study area.