# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 4 Results
### Pixel Statistical Analysis  
In the further analysis, the performance of each model based on the training/testing dataset with specific combinations of influencing factors was investigated, and the prediction results in terms of landslide susceptibility were statistically decomposed. Based on probability, the landslide susceptibility levels were classified into four grades [32]: very low susceptibility (0 \(\leq\) P(S) \(<\) 5%), low susceptibility (5% \(\leq\) P(S) \(<\) 35%), moderate susceptibility (35% \(\leq\) P(S) \(<\) 75%), and high susceptibility (75% \(\leq\) P(S) \(\leq\) 100%). Model performance was evaluated by analyzing the proportion of very low and high susceptibility levels. First, the susceptibility grade distribution of the models considering all the factors was analyzed. For example, according to Figure 10, the stacking model had the highest proportion within the high susceptibility grade at 40.52%, followed by the random forest (38.81%), XGBoost (30.01%), bagging (26.53%), CatBoost (24.18%), and LightGBM (22.93%) models. In regard to the very low susceptibility grade, the XGBoost model performed best with a proportion of 12.96%, followed by the random forest (10.60%) and CatBoost (9.31%) models, while the stacking model had the lowest proportion, at 7.22%. By optimizing the factor combination techniques of each model, the prediction of the susceptibility grades improved (Figure 11). For instance, the stacking model's proportion in terms of the high susceptibility grade increased to 42.54%, and the random forest model increased to 41.25%. Meanwhile,the XGBoost model's proportion in regard to the very low susceptibility grade rose from 12.96% to 13.57%. Overall, the uncertainty of all the models decreased. For example, the random forest model's proportion in regard to the high susceptibility grade increased from 38.81% to 41.25%, and the proportions for the LightGBM and CatBoost models also improved. However, the bagging strategy's proportion in regard to the high susceptibility grade decreased from 26.53% to 24.82%. In contrast, the stacking model demonstrated outstanding performance, significantly improving in regard to both high and very low susceptibility grades. Particularly in the very low susceptibility grade, its proportion increased from 7.22% to 14.35%. These results indicate that refined factor engineering strategies can effectively reduce model prediction uncertainty, thereby enhancing prediction accuracy. Figure 8: AUC value rankings after stepwise removal of low-importance factors: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model. Finally, the base models considering the optimal factors were used to predict landslide susceptibility across the entire region, and the prediction results were classified according to the susceptibility levels (Figure 12). The prediction results from the random forest model indicate that the low susceptibility category represents the highest proportion at 38.85%, followed by moderate susceptibility at 36.04%, very low susceptibility at 17.67%, and high susceptibility at 7.44%. The RF model demonstrates strong identification capabilities in regard to the low susceptibility category and slightly higher predictions in regard to the high susceptibility category compared to the other models. The XGBoost model's prediction results show that the very low susceptibility category represents the highest proportion at 25.99%, followed by moderate susceptibility at 33.01% and low susceptibility at 31.93%, with high susceptibility at 9.07%. The XGBoost model excels in identifying the very low susceptibility category and also shows relatively high predictions in regard to the high susceptibility category. The LightGBM model predicts the moderate susceptibility category most frequently, accounting for 43.82%, indicating its high sensitivity to moderate-risk areas.