## 2 Materials and Methods
### Landslide Susceptibility Modeling  
To fully achieve the goal of this study, we used various data for landslide susceptibility assessment. Namely, we generated the two landslide susceptibility models based on landslides located within the \"Lososina\" and \"Grodek\" regions in the first and second modeling scenarios, respectively. The qualitative information about the number of landslides used for modeling and its contribution in the total analyzed area is presented in Table 3. A graphical representation of the region separation is presented in Figure 4. Based on this, it can be observed that 46% and 54% of the total detected landslides were used for the LSM in \"Lososina\" and \"Grodek\" strategies, respectively. Moreover, it is apparent that there is an 8% difference in the landslides used for modeling between these two strategies. At this point, it is worth reiterating that 70% of the landslides are typically used for LSM in the literature [2; 3; 13; 18; 20; 21; 22; 24; 27]. Moreover, a random sampling strategy is used for assessing landslide susceptibility; the landslides used for modeling are distributed randomly and evenly across the investigated area, so the variety of conditions (LCFs) can be better captured. Considering this, in the first and second strategies, we used fewer landslides for modeling purposes. Thus, we supposed that this modeling of landslide susceptibility would have lower performance than that usually presented in the literature. However, the goal of the presented work was to investigate whether landslide inventory could be used for LSM in surrounding regions rather than to strive for accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variable** & **Data Used** & **Technique** & **Classification Method** & **References** \\ \hline elevation & DEM & — & Natural Jenks & [2; 3; 13; 14; 15; 19; 22; 26; 46] \\ \hline aspect & DEM & ArcGIS & Natural Jenks & [13; 14; 21; 22; 24; 26] \\ \hline slope & DEM & ArcGIS & Natural Jenks & [2; 3; 13; 14; 16; 19; 22; 24] \\ \hline curvature & DEM & ArcGIS & Natural Jenks & [3; 13; 15; 16; 18; 20; 21; 24] \\ \hline side exposure index (SEI) & DEM & [47] & Natural Jenks & [45] \\ \hline tectonics & geology map & — & — & [3; 13; 14; 19; 20; 22; 48] \\ \hline lithostratigraphic unit & geology map & — & — & [19; 27] \\ \hline fault proximity & geology map & EDB & Natural Jenks & [13; 19; 22; 24; 26] \\ \hline thrust proximity & geology map & EDB & Natural Jenks & [49; 50] \\ \hline distance to streams & DEM & EDB & Natural Jenks & [3; 19; 20] \\ \hline distance to lake & Roznów lake shapefile & EDB & Natural Jenks & [16; 24] \\ \hline compound topographic & & & & \\ index (CTI) & DEM & [47] & Natural Jenks & [13; 48; 51; 52] \\ \hline integrated moisture & & DEM & [47] & Natural Jenks & [46; 53] \\ index (IMI) & DEM & [47] & Natural Jenks & [45; 54] \\ \hline flow direction & DEM & [47] & Natural Jenks & [45; 54] \\ \hline precipitation & measurements from & & Natural Jenks & [13; 51; 55] \\ \hline distance to roads & road network & EDB & Natural Jenks & [3; 13; 14; 16; 19; 22; 24; 28; 51] \\ \hline Land Cover (LC) & Sentinel-2A & Supervised ML & — & [2; 3; 13; 28; 51] \\ \hline NDVI & Sentinel-2A & \(\frac{\text{NIR-REP}}{\text{NIR-REP}}\) where NIR is & & \\  & near inferred & & Natural Jenks & [3; 13; 24; 28; 51] \\ \hline  & & band (band 4) and RED & & \\  & is the red band (band 3) & & & \\ \hline soil suitability & digitized soil map & — & [3; 13; 21; 24; 28; 51] \\ \hline \end{tabular}
\end{table}
Table 2: Landslide conditioning factors used in presented study (ML: Machine learning).  
Figure 3: Examples of landslide conditioning factors utilized in this study (**a**) aspect, (**b**) fault proximity, (**c**) land cover, (**d**) CTI, (**e**) flow direction, (**f**) lake proximity, (**g**) tectonics, (**h**) precipitation, (**i**) NDVI, (**j**) road proximity, (**k**) SEI, (**l**) slope, (**m**) stream proximity, (**n**) thrust proximity, (**o**) elevation, (**p**) lithostriagraphic units, (**r**) soil suitability.