## 5 Discussions  
In this current study, the correlation analysis between conditioning factors and landslides was carried out by the CF method. The probability of landslide occurrence is in inverse correlation with elevation. This may be related to local rainfall and loess and may be related to human engineering activities. With the increase of slope angle, the degree of certainty of landslide occurrence decreases. This may be due to the larger slope angle, the less loose material or more weatherproof material. At the same time, it can be observed that most landslides occur on slopes facing south and southwest with the highest probability. This is mainly because more rain and sunshine are available to the south and landslides are prone to occur. The curvature of plan and profile shows anomalous results. The curvature of the plan (near zero) and convex plan (positive value) are highly sensitive. This anomaly may be related to the overweight effect [28; 99; 100]. In terms of land use, the probability of landslides in residential areas is the largest, which can explain the impact of human engineering activities on landslides. For the lithology, the second group (Tertiary (T): mudstone, conglomerate) and the fourth group (Triassic (T): mudstone, sandstone, songlomerate) are more sensitive to landslide occurrence. There is groundwater flow in the relatively fractured saturated sandstone and fractured conglomerates, resulting in additional load on the mudstone, resulting in landslides [28; 101]. The linear characteristics of the road and river buffers are inversely correlated with landslide susceptibility in the distance. Such an important result has been repeated in many kinds of literature [6; 102; 103; 104]. However, the remaining five variables make little contributions to the occurrence of landslides. According to ROC curve analysis (Figures 5 and 6) and statistical index analysis (Tables 4 and 5), it can be concluded that the four machine learning methods selected in the training and testing data assemble a very small \(p\)-value and significant high performance in the 95% confidence interval. The BFT \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pair** & **FT vs. BFT** & **FT vs. RFFT** & **FT vs. DFT** & **BFT vs. RFFT** & **BFT vs. DFT** & **RFFT vs. DFT** \\ \hline Chi-squared & 40.376 & 0.044 & 8.205 & 44.928 & 63.681 & 14.454 \\ \(p\)-Value & \textless{}0.0001 & 0.834 & 0.004 & \textless{}0.0001 & \textless{}0.0001 & 0.000 \\ Significance & Yes & No & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pairwise comparison of four models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Variables** & **FT Model** & **BFT Model** & **RFFT Model** & **DFT Model** \\ \hline ROC Curve Area & 0.836 & 0.947 & 0.839 & 0.797 \\ Standard Error & 0.021 & 0.011 & 0.020 & 0.023 \\ \(95\%\) Confidence Interval & 0.795–0.878 & 0.925–0.969 & 0.798–0.879 & 0.752–0.842 \\ \(p\)-Value & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of receiver operating characteristic (ROC) curves with the training dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Variables** & **FT Model** & **BFT Model** & **RFFT Model** & **DFT Model** \\ \hline ROC Curve Area & 0.745 & 0.804 & 0.740 & 0.748 \\ Standard Error & 0.039 & 0.035 & 0.0394 & 0.039 \\
95\% Confidence Interval & 0.668–0.822 & 0.736–0.871 & 0.663–0.817 & 0.672–0.824 \\ \(p\)-Value & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of ROC curves with the validation dataset. model has the highest AUC value (0.947), the lowest standard deviation (0.011), 95% confidence interval (0.925-0.969), and \(p\)-value (<0.0001). However, the DFT model has the worst results in this study area. The DFT model has the lowest AUC value (0.797), the highest standard deviation (0.023), 95% confidence interval (0.752-0.842), and \(p\)-value (<0.0001).