## 4 Results
### Landslide Susceptibility Assessment Using Ensemble Modeling  
As the main goal of this study is to examine the \"optimal\" ensemble on the basis of available candidates for landslide susceptibility assessment, the susceptibility maps of the study area are  
Figure 8: Scatter-plot matrixes of base learners (**a**) on 20 resamples, (**b**) on 30 resamples, (**c**) on 40 resamples. PCC: [PERSON]’s correlation coefficient.  
Figure 7: Density curves of base learners (**a**) on 20 resamples, (**b**) on 30 resamples, (**c**) on 40 resamples.  
developed mainly using four ensemble models. Once ensemble modeling was successfully implemented, landslide conditioning factors were fed into the model to produce predictions for the whole area. Thus, each pixel in the study area was allocated a unique landslide susceptibility index (LSI). The higher the index value, the more susceptible the location. Those values were then categorized into five levels, namely, very low, low, moderate, high, and very high by using the geometrical interval classification technique in ArcGIS 10.2 software. Inspection of Figure 9 shows that a similar distribution pattern of landslide susceptibility was gained for various models, especially for the SAN model and the ANL model. All ensemble models were in accordance with the in that areas with very high susceptibility classes were mainly distributed near the Nujiang River Grand Canyon where strong tectonic breaks disturb the terrain, and that the developed road system in conjunction with low vegetation coverage in low-elevation areas further promoted slope instability. Clay-bearing rocks (e.g., mudstones and shales) and sediments play an important role in slope sliding as they contribute to the infiltration process [24]. Additionally, carbonate rocks are likely to be located in highly vulnerable areas because they are prone to weathering which damages slope stability. The visual inspection of the geological map indicates that landslides were related to these negative geologic conditions.  
Statistical results on the percentage of landslide pixels (PL) and the percentage of pixels in a susceptibility class (PC) are summarized in Table 3. Regarding the SANL model, 69.85% of the total landslides were located in the very high class that accounted for 16.80% of the total study area, whereas 83.76% of the total landslides occurred in the very highly susceptible areas (22.90%) using the SAN model. As for ANL and SAL models, 79.38% and 57.73% of the total landslides fell into very highly vulnerable classes, while very high classes occupied 21.82% and 11.80% of the total study area, respectively.  
Furthermore, the reliability of landslide susceptibility mapping was evaluated using landslide density (LD) analysis. LD is expressed by a ratio of PL and PC [2]. It can be observed from Table 3 that  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**SANL**} & \multicolumn{3}{c}{**SAN**} & \multicolumn{3}{c}{**ANL**} & \multicolumn{3}{c}{**SAL**} \\ \cline{2-13}  & **PC (\%)** & **PL (\%)**  
LD values consistently increased from very low susceptibility level to very high susceptibility level for all ensemble models, indicating the developed landslide susceptibility assessment was reasonable.