## 4 Results  
### Weight of Related Factors and Habitat Potential Mapping  
The weights of the related factors between the layers acquired during the ANN model training process are shown in Table 3. The weight values show the contribution of each weight to the ANN model. Since the weights were initialized during the first training, the results could differ. This study attempted to obtain similar results by performing the calculation several times. The weight calculation was repeated for 10 epochs, with 100 cycles for each epoch, to identify the influence of the randomly extracted sample for a city/dry area (public area), non-habitat area, since most wild animals do not live in populated urban areas. No leopard cats were observed in public areas in the results of the frequency ratio analysis in a previous study [23]. In the iterations of each epoch, the weights were updated via back propagation. The standard deviation was 0.001-0.018 for martens and 0.002-0.021 for leopard cats. The ranges of standard deviations indicated that there were minimal effects of random sampling of non-habitat area on the results.  
Regarding the average weights of the related factors for the habitat potential mapping of martens, DEM had the highest value (0.166), followed by timber type (0.145), while land cover showed the lowest value (0.069). The factor weights for leopard cats were similar. Slope gradient had the highest value (0.194), followed by DEM (0.173), and land cover had the lowest value (0.072).  
However, for a comparison of the same standards between the two analyses, all average weight values were normalized by dividing by the smallest average weight value, land cover, for each factor for martens and leopard cats. The lowest values of 0.069 and 0.072 were normalized to 1.000. DEM showed the highest normalized weight value of 2.388 for martens and timber type was the second highest normalized factor at 2.095. Likewise, the weight analysis showed that the slope gradient and DEM presented the highest and second highest normalized weight values of 2.679 and 2.388 for leopard cats, respectively. The least important factors, land cover for both martens and leopard cats, were used as references for the normalized weights. Timber age (1.015) and slope aspect (1.299) for martens and slope aspect (1.062) and distance from road (1.129) for the leopard cats followed.  
The weights calculated via iteration, shown above, were applied throughout the study area, and the final weights determined for martens and leopard cats were applied to the corresponding factor in each dataset. Figure 5 shows the habitat potential maps for martens and leopard cats. To simplify the interpretation of the habitat potential maps, the results were classified into five classes of potentials: very high (10%), high (10%), medium (20%), low (20%), and very low (40%). Based on the habitat potential maps of both martens and leopard cats, the eastern region of South Korea appeared to have high potential.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Marten**} & \multicolumn{3}{c}{**Leopard Cat**} \\ \hline  & **Average** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Normalized Weight** \\ **with Respect to** \\ **Land Cover** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **with Respect to** \\ **Land Cover** \\ \end{tabular} &
\begin{tabular}{c} **Normalized Weight** \\ **with Respect to** \\ **Land Cover** \\ \end{tabular} \\ \hline DEM & 0.1657 & 0.0175 & 2.3877 & 0.1728 & 0.0208 & 2.3883 \\ Slope gradient & 0.1335 & 0.0102 & 1.9242 & 0.1939 & 0.0103 & 2.6790 \\ Slope aspect & 0.0901 & 0.0019 & 1.2986 & 0.07