## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Application and Evaluation of Blending Ensemble Model
#### 4.3.2 Weight Analysis of Landslide Predisposing Factors and Model Reliability Testing  
During the model evaluation process, the classification accuracy of each base model on the test set was first calculated, and they were quantified through common binary classification evaluation metrics such as precision, recall rate, and F1 value. To investigate the impact of the predisposing factor of rainfall on model evaluation, the factor of rainfall was initially excluded, and prediction results were computed based on the 15 predisposing factors. The importance coefficients of the factors and the prediction results of each model were as follows:  
It can be known from Figures 10-13 and Tables 5-8 the prediction results of the three base learners are used as input features to train the meta-learner, and a logistic regression model is employed to generate the final prediction. Ensemble models typically significantly improve predictive performance, as they combine the outputs of multiple models, reducing the risk of overfitting or underfitting that may arise from a single model. The importance coefficients of the ensemble model and the ROC curve are shown below:Figure 11: ROC Curve-XGBoost. Figure 12: ROC curve—CatBoost. Figure 13: ROC curve—Blending ensemble model. \begin{table}
\begin{tabular}{l c} \hline \hline
**PF** & **Importance Coefficients** \\ \hline Elevation & 0.149948 \\ YSC & 0.091733 \\ NDVI & 0.088044 \\ DSN & 0.086265 \\ YSEB & 0.066175 \\ Distance to roads & 0.065074 \\ Distance to rivers & 0.055843 \\ Plan curvature & 0.055399 \\ Soil types & 0.055156 \\ Slope & 0.053915 \\ Profile curvature & 0.050946 \\ Distance to faults & 0.050121 \\ Road density & 0.047683 \\ Aspect & 0.044365 \\ YSD & 0.039332 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Importance coefficients of predisposing factors (PF) in XGBoost. \begin{table}
\begin{tabular}{l c} \hline \hline
**PF** & **Importance Coefficients** \\ \hline Elevation & 0.153182 \\ NDVI & 0.144482 \\ Distance to faults & 0.079307 \\ DSN & 0.078497 \\ Slope & 0.072615 \\ Plan curvature & 0.072276 \\ Profile curvature & 0.0614889 \\ Aspect & 0.059107 \\ Road density & 0.056349 \\ YSC & 0.052362 \\ Distance to roads & 0.044927 \\ YSEB & 0.038630 \\ YSD & 0.037964 \\ Soil types & 0.032415 \\ Distance to rivers & 0.016397 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Importance Coefficients of predisposing Factors (PF) in CatBoost. \begin{table}
\begin{tabular}{l c} \hline \hline
**PF** & **Importance Coefficients** \\ \hline Elevation & 0.166398 \\ NDVI & 0.136907 \\ Slope & 0.104169 \\ Plan curvature & 0.071519 \\ Profile curvature & 0.069768 \\ DSN & 0.068317 \\ Aspect & 0.065359 \\ Distance to faults & 0.065237 \\ Road density & 0.053942 \\ YSEB & 0.047011 \\ Distance to road & 0.040264 \\ YSC & 0.039642 \\ YSD & 0.030077 \\ Soil types & 0.023435 \\ Distance to rivers & 0.017955 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Importance coefficients of predisposing factors (PF) in RF model. The regression coefficients of the meta-learner (logistic regression) can be used to analyze the contribution of each of the three models to the ensemble model. XGBoost is found to contribute the most to the ensemble model. To make an accuracy assessment of landslide prediction, a random sample of 100 landslide data points were used for prediction, with a threshold of 0.5. Predictions greater than 0.5 were classified as landslides (1), while those below 0.5 were classified as non-landslides (0). Among the 56 actual landslide points, 43 were correctly predicted, yielding an accuracy of 76.8%. For the 44 non-landslide points, 31 were correctly predicted, resulting in an accuracy of 70.5%. The models can be evaluated, as is indicated in Table 9.  
The weight analysis made above is based on the 15 predisposing factors without considering the dynamic landslide-inducing factor of rainfall.