## 5 Conclusions  
This study compared and analyzed landslide susceptibility at Woomyeon Mountain using different models. For this purpose, landslide-related spatial data consisting of a landslide inventory, and landslide explanatory variables were collected and prepared. The landslide inventory map was built using aerial photographs. The 14 landslide explanatory variables were constructed from spatial data collected by government organizations. These factors included altitude, slope degree, slope aspect, profile curvature, plan curvature, distance to streams, TWI, SPI, STI, TRI, timber type, timber diameter, timber age, and timber density. The contribution of each landslide explanatory variable was evaluated using the average IG value with a 10-fold cross-validation approach. All of the landslide explanatory variables contributed to the models, because the IG values of all factors were greater than zero. Therefore, the landslide susceptibility analysis and mapping were performed with all landslide explanatory variables using the RF and BRT models. The RF and BRT models were implemented in R. A popular open-source software, R is helpful for statistical computing and data visualization [58]. The models were constructed using optimized parameters, and LSI was predicted over the study area. The LSMs produced in this study may prove useful for decision makers, planners, and engineers in disaster planning to minimize economic losses and casualties. In a future study, the accuracy of the LSMs of this study could be enhanced by selecting more optimal landslide explanatory variables and solving the problem of overfitting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Random Forest**} & \multicolumn{2}{c}{**Boosted Regression Tree**} \\ \cline{2-5}  & **Importance** & **Rank** & **Importance** & **Rank** \\ \hline Terrain roughness index & 1.000 & 1 & 1.000 & 1 \\ Distance to streams & 0.857 & 2 & 0.556 & 3 \\ Altitude & 0.766 & 3 & 0.277 & 5 \\ Sediment transport index & 0.654 & 4 & 0.562 & 2 \\ Timber type & 0.484 & 5 & 0.158 & 7 \\ Slope degree & 0.469 & 6 & 0.000 & - \\ Stream power index & 0.449 & 7 & 0.084 & 9 \\ Topographic wetness index & 0.440 & 8 & 0.378 & 4 \\ Slope aspect & 0.408 & 9 & 0.242 & 6 \\ Plan curvature & 0.214 & 10 & 0.099 & 8 \\ Profile curvature & 0.118 & 11 & 0.016 & 10 \\ Timber diameter & 0.026 & 12 & 0.000 & - \\ Timber age & 0.009 & 13 & 0.000 & - \\ Timber density & 0.000 & 14 & 0.000 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative importance of each landslide explanatory variable calculated in the random forest and boosted regression tree model. **Author Contributions:** [PERSON] analyzed the data and wrote the paper. [PERSON] suggested the idea. **Funding:** This research was financially supported by the BK21 Plus Project of the Graduate School of Earth Environmental Hazard System. In addition, this work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017R1A2B2009033). **Conflicts of Interest:** The authors declare no conflicts of interest. Figure A2: Cont. **Figure A2. ** Landslide explanatory variables used to analyze landslide susceptibility: (**a**) altitude, (**b**) slope degree, (**c**) slope aspect, (**d**) profile curvature, (**e**) plan curvature, (**f**) distance to streams, (**g**) topographic wetness index, (**h**) stream power index, (**i**) sediment transport index, (**j**) terrain roughness index, (**k**) timber type, (**l**) timber diameter, (**m**) timber age, and (**n**) timber density. **Table A1. ** Correlations between landslide and landslide explanatory variables using the frequency ratio.