## 4 Discussion  
Based on a comparison of the SCAI indices, the general performance (in terms of accuracy) of both models can be described as good. For the validation study area, the SCAI index was higher than 50 and lower than 0.5 for the very low and very high susceptibility classes, respectively. This shows higher performance than that described in other studies [13; 14; 55]. The \(R_{ind}\) calculated for the validation area shows that 46% and 57% of landslides fall into the high and very high susceptibility zones in the \"Grodek\" and \"Lososina\" models, respectively. This implies that besides the SCAI index, the \(R_{ind}\) should also be used to evaluate the LSM. The explanation for the low value of \(R_{ind}\) for the high and very high classes is that 37% and 45% of the landslides are located in the moderate class in the \"Lososina\" and \"Grodek\" models. This indicates that another classification method should be \begin{table}
\begin{tabular}{c c c} \hline  & **Difference between Models of “Lososina”and “Grödek”** \\ \cline{2-3}  & **Pixels** & **Area Percentage [\%]** \\ \hline no difference & 13,031,043 & 47 \\ one-zone difference & 14,623,961 & 42 \\ two-zones difference & 3,219,848 & 10 \\ three-zones difference & 405,072 & 1 \\ four-zones difference & 751 & 0 \\ \hline Correlation (\(r\)) & 0.697 & \\ \hline \end{tabular}
\end{table} 
Table 6: Difference between two susceptibility models. Figure 8: Difference between “Lososina” model and “Grödek” model for (**a**) Landslide Susceptibility Index (LSI) and (**b**) susceptible class (SC). considered to effectively differentiate the susceptibility zones (e.g., quantiles, IsoData). This issue has been discussed in [60]. When comparing these two models, the SCAI indexes for Lososina model are more satisfactory. This is mostly due to the high SCAI values for very low and low susceptible zones and very low values for high and very high susceptible zones. Moreover, relative landslide density (\(R_{ind}\)) is higher for Lososina model (72%). Therefore, this model is more reliable than the Grodek model (\(R_{ind}=68\%\)). This is surprising because it was expected that the \"Grodek\" model would better predict landslide susceptibility due to the greater amount of landslide used for modeling (54%) when compared with \"Lososina\" model (46%). Nevertheless, this indicate that the greater amount of landslides used for modeling does not go hand in hand with improved landslide susceptibility assessment. As we mentioned in the Introduction section, scientists applied a randomly selected 70% of the landslides for modeling and 30% for validation. In the selection strategy, the landslides used for the modeling are distributed across the study area. Instead of our sampling strategy (splitting the area for the modeling and validation regions), landslides distributed evenly across the image can better \"capture\" a variety of topographical, geological and other environmental conditions (LCFs). When the study area is split into modeling and validation areas, it is possible for some landslide-prone geological units to be absent from the area used for modeling. Additionally, by comparing Figure 8 with LCFs, it can be observed that differences of up to two or three susceptible zones occur in the areas of faults and thrust proximity (intensive red and blue color). These differences of two classes can also be observed in changes of lithostriagraphic units (intensive red and blue color). This additionally confirmed our notion that the landslide sampling strategy had a crucial effect on the accuracy of LSM. To better investigate the difference between these two models, a correlation matrix between the difference in the LSI index (Figure 8a) and each LCF was calculated and is presented in Figure 9. Based on this, it can be concluded that this difference between the models highly corresponds to the fault, lake, and trust proximity.