## 5 Results and Discussion
### Model Training and Evaluation  
Using the eight predisposing factors, the BE-LMTree model was trained using the training dataset with the 10-fold cross validation technique. The training result is shown in Figure 7. It could be seen that the CLA of the BE-LMTree model is 93.81%, indicating a high degree of fit of the model with the dataset. Kappa statistics of 0.876 indicates the high agreement of the model and the training dataset. SEN of the BE-LMTree model is 93.02%, indicating that the proportion of the landslide pixels is correctly classified to the landslide class is 93.02%. Whereas, SPE is 94.63%, indicating that the proportion of the non-landslide pixels is correctly classified to the non-landslide class is 94.63%. PP2 is 94.72%, indicating that the probability that the BE-LMTree model correctly classifies pixels to the landslide class is 94.72%. NP2 is 92.89% indicating that the probability the BE-LMTree model correctly classifies pixels to the non-landslide class is 92.89%. Overall, these above measures have demonstrated that the BE-LMTree model performed very well with the training dataset. To assess the contribution of landslide factors to the BE-LMT model, each factor was removed, and then, the classification accuracy (CLA) was estimated. The reduction of CLA of the BE-LMT model when one or more factors were removed indicates the contribution of these factors to the model. The result is shown in Table 2. It could be seen that when Distance to Fault and Soil type were removed from the LMT model, the CLA was reduced 2.12%. Therefore, although the average merit of Distance to \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Predisposing Factors** & **Average Merit** & **Standard Deviation** \\ \hline
1 & Slope & 0.225 & 0.008 \\
2 & Distance to river & 0.171 & 0.008 \\
3 & Lithology & 0.148 & 0.008 \\
4 & Aspect & 0.129 & 0.008 \\
5 & Elevation & 0.102 & 0.006 \\
6 & Land cover & 0.077 & 0.008 \\
7 & Distance to fault & 0.055 & 0.005 \\
8 & Soil type & 0.038 & 0.005 \\ \hline \end{tabular}
\end{table} 
Table 1: Predictive ability of eight landslide predisposing factors using Pearson technique and 10-fold cross validation techniques. fault (0.055) and Soil type (0.038) are small (see Table 1), the two factors contributed to 2.12% increasing classification accuracy of the BE-LMT model. An even larger accuracy decrease (4.3%, see Table 2) occurred when the four most significant variables (Slope, Distance to river, Lithology, and Aspect) are used into the BE-LMT model. Overall, it is reasonable of the to keep all factors in this research. The prediction performance of the BE-LMTree model is assessed using the validation dataset and the result is shown in Figure 8. It could be observed that the CLA is 87.89%, indicating a high prediction result. Kappa statistics of 0.759 indicates that the prediction performance of the model is 75.9% better than random. SEN of the BE-LMTree model is 92.25%, indicating that the proportion of the landslide pixels, which is accurately predicted, is 92.25%. SPE of the BE-LMTree model is 84.35%, indicating that the proportion of the non-landslide pixels is accurately predicted is 84.35%. PP2 of the model is 82.73%, indicating that the probability that the BE-LMTree model accurately predicts pixels to the landslide class is 82.73%. NP2 is 93.05%, indicating that the probability that the BE-LMTree model accurately predicts pixels to the non-landslide class is 93.05%. Figure 9 shows 72 mispredicted landslide pixels (false positive) and 29 mispredicted non-landslide pixels (false negative) for the study area. We see that the 76.4% and 20.8% of the mispredicted landslide pixels were located in areas with slope angles <8.86\({}^{\circ}\) or slope angles from 36.39\({}^{\circ}\) to 5.87\({}^{\circ}\), respectively.