## 3 Results
### Model Performance Analysis  
The model's performance was evaluated by analyzing the classification report for flood-prone areas as presented in Table 2. The classification report provided a comprehensive summary of the performance of the XGBoost classification model. In Table 2, it can be observed that the XGBoost model achieved exact classification for class 0 (representing non-flooded areas), with both precision and recall close to 1. However, a slight decrease in performance was noted for class 1 (flooded areas), where precision and recall were 84% and 96%, respectively. This performance discrepancy was attributed to class imbalance, as the support for class 0 is significantly higher (i.e., 1,025,456) compared to class 1 (i.e., 11,150). It is important to emphasize that class imbalance should not be considered a drawback of the modeling process, as it naturally reflects the distribution of flood-prone and non-flood-prone areas within the basin. The number of flood-prone areas is inherently smaller than that of those regions where flooding does not occur. The initial labeling map used for classification represented a snapshot of the study area at a specific point in time. Further analysis of the classification report indicated that the model demonstrated strong overall performance, as evidenced by high accuracy, macro average, and weighted average values. With a total of 1,036,606 instances considered, these metrics suggested the robustness and reliability of the XGBoost model in classifying flood-prone areas. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classification Report**} \\ \hline  & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
0 & 0.999 & 0.998 & 0.999 & 1,025,456 \\
1 & 0.837 & 0.958 & 0.893 & 11,150 \\ accuracy & & & 0.997 & 1,036,606 \\ macro avg & 0.918 & 0.978 & 0.946 & 1,036,606 \\ weighted avg & 0.998 & 0.998 & 0.998 & 1,036,606 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification report for flooded area. Figure 5: Correlation matrix: (**a**) flooded areas; (**b**) landslide areas. As shown in Table 2, it is evident that the model shows extremely high performance in classifying flood events--with an accuracy of 0.997--raising concerns about the possible presence of overfitting. To assess the model's generalization capability and verify this hypothesis, a systematic performance analysis was carried out across different validation levels (Table 3). The results obtained from the 10-fold cross-validation show a remarkable stability of the metrics across the different folds: precision ranges from 0.801 to 0.819, recall from 0.964 to 0.968, F1-score from 0.878 to 0.887, and accuracy remains 0.997. This consistency across folds indicated that the model did not excessively fit the training data specific to each fold, suggesting a robust and generalizable learning process. These findings were further confirmed by the independent test set (30% of the data, not used during training), where the model achieved high values in line with those observed in the cross-validation. In the presence of overfitting, one would expect significantly higher metrics on the training/CV phases compared to the test set; however, in the current study, no performance drop was observed. On the contrary, the metrics on the test set were evenly higher than the average from cross-validation (e.g., mean F1-score of 0.882), further supporting the hypothesis of a well-generalized model. An additional contribution to mitigating overfitting came from the use of the SMOTE (Synthetic Minority Over-sampling Technique) during the training phase. This technique helped balance the classes by generating synthetic examples of the minority class, improving the model's fairness and generalization capability.