## 3 Results
### ML Prediction and Verification  
According to the five machine learning methods used in this research, the relevant parameters were set as shown in Table 2. In these machine learning calculations, three years of spatial factor data (from 2007 to 2009) were used as input for learning, and the landslide prediction of the slope units was based on the 2010 spatial factors. Finally, the landslide location announced by the Central Geological Survey (MOEA, Taiwan) in 2010 was used to verify the accuracy of slope unit prediction. The prediction accuracy of machine learning prediction is shown in Table 3. From the correct rate, the addition of the InSAR factor increased the accuracy of prediction by 0% to 6%. For Siaolin Village, the random forest method had the highest prediction accuracy rate (82.95%), followed by XGBoost (79.31%), AdaBoost (78.49%), naive Bayes (70.93%), and DT (68.02%). Putunpunas River showed a similar trend; the best prediction was observed from the random forest method (80.51%), followed by XGBoost (78.80%), AdaBoost (75.64%), DT (68.19%), and naive Bayes (68.19%). The prediction results of the best learning method (random forest) were used to compare and evaluate the predicted classification through confusion matrixes. In Figure 6, the correctly predicted landslide slope units are colored red, and the correctly predicted noncollapsed slope units are colored cyan. In addition, the erroneously predicted landslide slope units are marked with green diagonal stripes, and the erroneously predicted nonlandslide slope units are marked with red diagonal stripes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML** & **Parameters** & **Values** \\ \hline Naive Bayes & Smoothing & \(10^{-9}\) \\ \hline \multirow{4}{*}{DT} & Criterion & Gini \\  & The maximum of depth & 20 \\  & The minimum of samples split & 10 \\  & The minimum of samples leaf & 5 \\ \hline \multirow{4}{*}{Random Forest} & Criterion & Gini \\  & The maximum of depth & 20 \\  & The minimum of samples split & 2 \\  & The minimum of samples leaf & 5 \\  & The number of estimators & 100 \\ \hline \multirow{4}{*}{AdaBoost} & Criterion & Gini \\  & The maximum of depth & 20 \\  & The minimum of samples split & 2 \\  & The minimum of samples leaf & 5 \\  & The number of estimators & 10 \\  & Algorithm & SAMME \\  & Learning rate & 0.1 \\ \hline \multirow{4}{*}{XGBoost} & The maximum of depth & 5 \\  & The number of estimators & 1000 \\ \cline{1-1}  & Learning rate & 0.1 \\ \cline{1-1}  & The minimum of child weight & 1 \\ \cline{1-1}  & Gamma number & 0 \\ \cline{1-1}  & Subsample number & 0.8 \\ \cline{1-1}  & Colsample bytes & 0.8 \\ \cline{1-1}  & Objective binary & Logistic \\ \cline{1-1}  & nthread & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters and settings required for the machine learning methods. The confusion matrixes of Siaolin Village and Putunpunas River are shown in Tables 4 and 5. In Siaolin Village, the correct prediction rates of landslide and noncollapsed slope units were 78.72% and 94.30%, respectively; the average accuracy rate of the overall prediction was 82.95%. In Putunpunas River, the correct prediction rates of landslide and noncollapsed slope units were 89.67% and 66.18%, respectively; the average accuracy rate of the overall prediction was 80.52%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual** & **Predicted** & **Lanslide** & **Nonlanslide** & **Average** \\ \hline Lanslide & 74 (TP) & 2 (FN) & - \\ Nonlanslide & 20 (FP) & 33 (TN) & - \\ Correct rate (\%) & 78.72 & 94.30 & 82.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the case of the Siaolin Village analysis. Figure 6: Visual illustration of the landslide prediction results—(**left**) Siaolin Village; (**right**) Putunpunas River area.