## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Application and Evaluation of Blending Ensemble Model  
#### 4.3.1 Sample Training  
In landslide prediction, the objective is to forecast the probability of landslide occurrence based on influencing factors. This is typically a binary classification task, where the prediction outcome is whether a landslide occurs or not. To improve prediction accuracy, a Blending ensemble learning method is adopted, combining three base learners--Random Forest (RF), XGBoost, and CatBoost--along with logistic regression as the meta-learner--to form an integrated model for landslide prediction.  
A crucial step in model training is data preprocessing, during which the three-sigma rule was applied to eliminate outliers, and min-max normalization was used to standardize the data, ensuring that different features were on the same scale to avoid model bias caused by dimensional differences. Additionally, to prevent tree models from overemphasizing normal cases (non-landslides) due to sample imbalance, the dataset was balanced by selecting 966 sample records (483 landslide points and 483 non-landslide points).  
The dataset was divided into a training set for model training and a test set to evaluate model performance at a ratio of 8.2. To construct the base learners, Random Forest (RF) was first employed for training. By integrating multiple decision trees, Random Forest effectively handles nonlinear relationships and provides strong generalization capabilities. Next, the XGBoost model was used. As a gradient-boosted tree model, XGBoost can process large-scale data and reduce overfitting risks, demonstrating strong performance in landslide prediction. Finally, the CatBoost model was adopted. It is a gradient-boosting model particularly suited for handling categorical features, with the ability to automatically process such features, making it suitable for complex datasets.  
After being individually trained, the predictive performances of the three base models (Random Forest, XGBoost, and CatBoost) were compared to evaluate the accuracy of each model. To further enhance prediction effectiveness, this paper introduces ensemble learning methods. In the ensemble learning framework, logistic regression serves as the meta-learner, integrating the predictions of the base learners to make the final classification decision. As a linear model, logistic regression combines the outputs of each model through a simple weighted sum and decision rule, thereby leveraging the strengths of each base learner.  
Target encoding is a method for converting categorical variables into numerical features. Its core idea is to replace the original categorical values with statistical properties of the target variable (e.g., mean, probability) corresponding to each category, thereby effectively handling non-ordinal categorical data. In the study, a smoothed target encoding approach (Equation (9)) is adopted. This method preserves the distributional characteristics of different categories while mitigating overfitting during the encoding process.  
\[Smooth\ Target\ Encoding(c_{i})=\frac{N_{c_{i}}\times Mean(c_{i})+m*Global Mean}{N_{c_{i}}+m} \tag{9}\]  
The categorical features are shown in Table 4.