## 4 Results
### Landslide Susceptibility Mapping Results  
In the case of LR model, LSI ranges from 0.11 to 0.96, and the corresponding area percentages were 40.17% (very low), 12.24% (low), 5.54% (moderate), 17.75% (high), and 24.30% (very high), respectively. Similarly, five reclassified classes of the CatBoost model accounted for 30.16%, 23.81%, 15.21%, 26.82%, and 14.94%, respectively, of the entire area. LSM constructed by the CNN model was also divided into five classes: very low (<0.18), low (<0.42), moderate (<0.653), high (<0.82), and very high (>0.82), accounting for 28.99%, 19.65%, 14.21%, 9.19%, and 27.96% of the whole area. It is noticed that LR, CatBoost, and CNN models predicted the largest proportion of very low susceptibility while IV predicted the largest proportion of very high susceptibility. A logical landslide susceptibility map should meet two rules: (1) the density of landslide samples should increase with the increase of susceptibility class and be mainly \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **DTF** & **MED** & **DTS** & **Elevation** & **Plan Curvature** & **Slope** \\ \hline Gini index & 5.36 & 5.00 & 4.80 & 4.10 & 3.69 & 2.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Conditioning factors assigned by the Gini index. Figure 7: Parametric importance graphics designed by Gini index. located in the highest susceptibility area; (2) the landslide susceptibility map should be spatially continuous and smooth, and the very high-susceptibility class area should occupy a small proportion. Concomitant with these maps (Figure 8), the landslide samples (dark spots) were mainly located in the red areas, and the non-landslide samples (blue spots) were in the green areas. The high or very high susceptibility areas were concentrated in the south of the areas, which was consistent with the distribution of landslides (Figure 9). Thus, the maps predicted by these models were logical on the whole. There are distinct differences among the landslide susceptibility maps derived from the models. Table 6 showed that the IV increased as the susceptibility class increased for the model and the IVs were all negative for the very low, low, and moderate susceptibility class while positive for the very high susceptibility class. For high susceptibility, the IVs were positive for LR, CatBoost, and CNN models while negative for the IV model. On the other hand, the maps predicted by IV and LR models were spatially discontinuous while CatBoost and CNN models produced smoother patterns. Moreover, the percentage of very high susceptibility class was similar for LR, CatBoost, and CNN models, while the IV model predicted the highest percentage, reaching 37.27%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Class**} & **Percentage of** & **Percentage of Landslide** & **IV** \\  & & **Area (\%)** & **Area (\%)** & **Area (\%)** & \\ \hline \multirow{4}{*}{IV} & Very low & 32.05\% & 8.06\% & \(-\)1.74 \\  & Low & 8.64\% & 7.66\% & \(-\)1.19 \\  & Moderate & 7.96\% & 5.04\% & \(-\)0.50 \\  & High & 14.08\% & 20.36\% & \(-\)0.14 \\  & Very high & 37.27\% & 58.87\% & 0.70 \\ \hline \multirow{4}{*}{LR} & Very low & 40.17\% & 5.24\% & \(-\)1.60 \\  & Low & 12.24\% & 4.44\% & \(-\)0.47 \\  & Moderate & 5.54\% & 6.05\% & \(-\)0.10 \\  & High & 17.75\% & 21.17\% & 0.14 \\  & Very high & 24.30\% & 63.10\% & 0.88 \\ \hline \multirow{4}{*}{CatBoost} & Very low & 30.16\% & 5.24\% & \(-\)1.74 \\  & Low & 14.42\% & 4.44\% & \(-\)1.18 \\  & Moderate & 7.82\% & 6.05\% & \(-\)0.25 \\  & High & 20.28\% & 21.17\% & 0.04 \\  & Very high & 27.31\% & 63.10\% & 0.84 \\ \hline \multirow{4}{*}{CNN} & Very low & 28.99\% & 4.44\% & \(-\)1.88 \\  & Low & 19.65\% & 8.67\% & \(-\)0.82 \\ \cline{1-1}  & Moderate & 14.21\% & 12.30\% & \(-\)0.14 \\ \cline{1-1}  & High & 9.19\% & 11.09\% & 0.19 \\ \cline{1-1}  & Very high & 27.96\% & 63.51\% & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The IV of different landslide susceptibility levels.