## 5 Results and Discussion
### The Landslide Susceptibility Map  
The final BE-LMTree model derived from the training step above was then used to compute landslide susceptibility indices for the Upper Reaches Area of Red River Basin (URRB), Vietnam. Accordingly, all of the predisposing factors in the raster maps were converted into ASCII format, and then fed to the BE-LMTree model to generate susceptibility indices. Distribution of these susceptibility indices is shown in Figure 11.  
These landslide susceptibility indices were then transformed to the raster format to manage in ArcGIS software using a python application that was programmed by the authors. Finally, the landslide susceptibility map (Figure 12) for the URRB was cartographically presented by five classes: very high (10%), high (10%), moderate (15%), low (25%), and very low (40%). To determine the thresholds for these classes, the extensively used graphic curve method has been considered to be the most suitable; a detailed explanation of it is available in [87; 97; 98]. The thresholds for these classes were determined based on an analysis of the susceptibility index map and the landslide inventory map, and then, the percentage of the landslide pixel versus the percentage of the susceptibility indices was calculated. At last, the four thresholds for the five classes were obtained.  
Characteristics of the five landslide susceptibility classes that were derived from the BE-LMTree model the study area are shown in Table 3. Accordingly, the overall landslide frequency (OLF) proposed in [99] for the five classes was derived, and theoretically, the overall frequency should gradually grow from the very low class to the very high class [87]. It can be seen that the very high occupied only 10% of the study area, but it has the highest OLF value (4.40), followed by the high class (OLF = 1.59), the moderate class (OLF = 0.86), the low class (OLF = 0.43), and the very low class (OLF = 0.41). These confirm that the BE-LMTree model performed well with the URRB area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Index Interval**} & \multicolumn{2}{c}{**Landslide**} & \multirow{2}{*}{**Expression**} & \multicolumn{2}{c}{**Overall Landslide**} & \multirow{2}{*}{**Areas (km2)**} \\  & & & **Susceptibility (\%)** & & & **Frequency (OLF)** & \\ \hline
1 & 1.000–0.981 & 90–100 & Very high & 4.40 & 327.4 \\
2 & 0.965–0.980 & 80–90 & High & 1.59 & 327.4 \\
3 & 0.925–0.964 & 65–80 & Moderate & 0.86 & 491.0 \\
4 & 0.795–0.924 & 40–65 & Low & 0.43 & 818.4 \\
5 & 0.000–0.794 & 0–50 & Very low & 0.41 & 1309.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the landslide susceptibility classes derived from the BE-LMTree model the study area.  
Figure 11: Distribution of these susceptibility indices versus of the five susceptibility classes.  
Visual interpretation of the map (Figure 12) shows that the high probability of landslide is for areas i.e., Sapa, Bat Xat, and Bao Yen, therefore these areas should receive more attention in the development of remedial measures for the landslide prevention. Inversely, the low probability of landslide is for the Van Ban area. In fact, this area belongs to the Hoang Lien National Park, which is covered by the protected and dense tropical forest [100], therefore, having a low probability of landslide.