## 4 Results and Discussion
### Evaluation of Models Performance  
The models were trained using the training dataset and after that were validated using the validation dataset. In the training phase (Table 2), the single HP and ensemble Decorate-HP models achieved the highest possible value of the PPV metric (100%), which indicated that the values of these two models had the highest capability to distinguish between landslide pixels and other pixels across the area. In terms of the NPV metric, the ensemble RABHP model with a value of 85.94% performed the best in distinguishing between non-landslide pixels and other pixels across the area. For the SST metric that measures the model's ability to correctly assign the landslide pixels to the landslide class, the RABHP model with a value of 87.50% was the best model. Regarding the SPF metric that measures the model's ability to correctly classify the non-landslide pixels in the non-landslide class, the single HP and ensemble Decorate-HP models with a value of 100% were dominant over the other models. In terms of the ACC metric that indicates the overall model accuracy, the RABHP model (92.19%) was identified as the most precise model over the training phase. Further, this model achieved the highest agreement (Kappa = 0.844) between observations and predictions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Average Merit** & **Error (AM)** & **Average Rank** & **Error (AR)** \\ \hline Distance from roads & 82.3 & 1.504 & 1.2 & 0.4 \\ Elevation & 80.552 & 1.283 & 1.8 & 0.4 \\ River density & 72.915 & 3.072 & 3.3 & 0.64 \\ Weathering crust & 68.75 & 0.931 & 4.4 & 0.49 \\ Fault density & 67.186 & 3.507 & 4.4 & 0.92 \\ Geology & 57.285 & 4.278 & 6.7 & 1.27 \\ TWI & 54.946 & 2.647 & 7.2 & 1.17 \\ Slope & 52.08 & 3.532 & 8.5 & 1.02 \\ Aspect & 52.595 & 3.414 & 8.6 & 1.02 \\ Curvature & 50.782 & 3.799 & 8.9 & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factor significance measured using the One-R technique. To evaluate the practicability of the six proposed models, they need to be tested with the unseen data (i.e., validation dataset), resulting in computing the prediction rate of the models. In the validation phase (Table 3), while the single HP and ensemble Decorate-HP models reached the maximum values of the PPV (92.86%) and SPF (88.24%) indices, the ensemble BHP model gained the maximum values of NPV (85.71%), SST (85.19%), ACC (83.93%), and Kappa (0.679). Many machine learning researchers have previously reported the asymmetric performances of different models when applied to the different training and validation datasets [24; 96]. The most logical explanation for these different performances is that different methods were developed based upon different computational algorithms, and therefore show different performances dealing with different datasets. For example, [PERSON] [97] suggested that the main reason for AdaBoost's failure is overfitting. According to [PERSON] [75], Bagging perform the best if the base model is unstable. In the matter of the magnitude of the training and validation error, the ensemble ABHP and Decorate-HP models with the lowest and highest RMSEs were identified as the most and least accurate models, respectively. The RMSEs ranged from 0.334 (ABHP) to 0.495 (Decorate-HP) in the training phase and from 0.362 (ABHP) to 0.496 (Decorate-HP) in the validation phase of the modeling process (Figure 5). The overall performance of the models proposed for the for the Nam Dan commune was evaluated by the ROC method that showed that all six models had excellent training and validation performances. More specifically, a comparative analysis of the AUC values derived from the models indicated that the ensemble RABHP model with an AUC of 0.968 had the best training performance (i.e., fitness to the training data), followed by the BHP, Dagging-HP, ABHP, Decorate-HP, and HP models that achieved the AUC values of 0.95, 0.937, 0.933, 0.895, and 0.895, respectively (Figure 6a).