## 4 Results
### Application of Landslide Susceptibility Models  
In this study, the training data and CF values were used to construct four models, namely the functional trees (FT) model, bagging-functional trees (BFT) model, rotation forest-functional trees (RFFT) model, and dagging-functional trees (DFT) model, respectively. To get the best performance of the model, the iteration times of the FT model and the minimum number of instances considering the separation of nodes from the training dataset are optimized to 15 and 36, respectively. When building the BFT, RFFT, and DFT models, the two parameters mentioned above were fixed firstly. After completing \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditioning Factors** & **Classes** & **Percentage of Landslide** & **Percentage of Domain** & **CF** \\ \hline \multirow{4}{*}{SPI} & 0–10 & 20.11 & 32.46 & \(-\)0.381 \\  & 10–20 & 25.00 & 19.69 & 0.212 \\  & 20–30 & 17.39 & 13.57 & 0.220 \\  & 30–40 & 10.33 & 8.20 & 0.206 \\  & \textgreater{}40 & 27.17 & 26.08 & 0.040 \\ \hline \multirow{4}{*}{TWI} & 1.11–2 & 55.43 & 56.33 & \(-\)0.016 \\  & 2–3 & 39.67 & 33.16 & 0.164 \\  & 3–4 & 3.80 & 7.36 & \(-\)0.483 \\  & 4–5 & 1.09 & 2.83 & \(-\)0.617 \\  & \textgreater{}5 & 0.00 & 0.31 & \(-\)1.000 \\ \hline \multirow{4}{*}{NDVI} & (\(-\)0.15)–0.01 & 14.13 & 13.96 & 0.012 \\  & 0.01–0.04 & 11.41 & 16.94 & \(-\)0.326 \\  & 0.04–0.07 & 16.85 & 22.45 & \(-\)0.250 \\  & 0.07–0.09 & 35.33 & 27.44 & 0.223 \\  & 0.09–0.31 & 22.28 & 19.20 & 0.138 \\ \hline \multirow{4}{*}{Land use} & Farmland & 25.54 & 36.96 & \(-\)0.309 \\  & Forestland & 20.11 & 18.93 & 0.059 \\  & Grassland & 53.80 & 43.70 & 0.188 \\  & Water bodies & 0.00 & 0.10 & \(-\)1.000 \\  & Residential areas & 0.54 & 0.29 & 0.465 \\  & Others & 0.00 & 0.02 & \(-\)1.000 \\ \hline \multirow{4}{*}{Lithology} & 1 & 60.33 & 75.17 & \(-\)0.197 \\  & 2 & 21.74 & 12.38 & 0.430 \\  & 3 & 0.54 & 0.94 & \(-\)0.421 \\  & 4 & 12.50 & 6.69 & 0.465 \\  & 5 & 4.89 & 4.82 & 0.015 \\ \hline \multirow{4}{*}{Soil} & Cultivated loessali soils & 76.63 & 85.66 & \(-\)0.105 \\  & Alluvial soils & 15.22 & 11.83 & 0.223 \\  & Red clay soils & 8.15 & 2.35 & 0.712 \\  & Water & 0.00 & 0.15 & \(-\)1.000 \\ \hline \multirow{4}{*}{Distance to roads (m)} & 0–100 & 27.72 & 15.20 & 0.452 \\  & 100–200 & 17.39 & 11.42 & 0.344 \\ \cline{1-1}  & 200–300 & 11.96 & 11.35 & 0.050 \\ \cline{1-1}  & 300–400 & 6.52 & 8.93 & \(-\)0.270 \\ \cline{1-1}  & \textgreater{}400 & 36.41 & 53.10 & \(-\)0.314 \\ \hline \multirow{4}{*}{Distance to rivers (m)} & 0–200 & 69.02 & 28.64 & 0.585 \\ \cline{1-1}  & 200–400 & 14.67 & 25.39 & \(-\)0.422 \\ \cline{1-1}  & 400–600 & 9.24 & 22.38 & \(-\)0.587 \\ \cline{1-1}  & 600–800 & 3.26 & 15.61 & \(-\)0.791 \\ \cline{1-1}  & \textgreater{}800 & 3.80 & 7.98 & \(-\)0.523 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._the above work, the optimized models were applied to the whole research area to create landslide susceptibility maps. The calculated landslide sensitivity index (LSI) values can be interpreted as the probability in the range of 0 and 1, and all LSI values can be converted to ArcGIS to generate the final landslide susceptibility map. Four landslide susceptibility maps generated by FT, BFT, RFFT, and DFT models are shown in Figure 3a-d respectively. The landslide susceptibility maps were reclassified into five classes, namely very low, low, moderate, high, and very high using the natural break method [92]. The comparison of area sizes for each category of the four models is shown in Figure 4. For the FT model, the largest area is the very low class (27.92%), followed by high class (23.47%), very high class (20.21%), low class (17.55%), and the smallest area is the moderate class (10.86%). For the BFT model, the percentages of very low, low, moderate, high, and very high classes are 24.02%, 22.87%, 19.88%, 18.10%, and 15.12%, respectively.