## 3 Results and Discussion
### Ablation Experiment: Contribution of Rainfall Factor  
To assess the specific contribution of post-seismic rainfall in co-seismic landslide susceptibility modeling, an ablation study was performed. The rainfall predictor, primarily based on cumulative rainfall for April to June 2015, was removed from the dataset, and the RF, XGBoost, and LR models were retrained using the reduced feature set. The performance of the modified models was then systematically compared with their full-feature counterparts to quantify the predictive value of rainfall in landslide triggering under post-seismic conditions.  
The results (Table 5) show that the exclusion of the rainfall feature led to a noticeable decline in all evaluation metrics, particularly ROC AUC, recall, and F1 score, across all models. This decline confirms that rainfall, despite being a secondary triggering factor following seismic activity, plays a substantial role in the physical processes underlying landslide initiation. Specifically, RF's AUC dropped from 0.94 to 0.93, while its recall decreased from 0.909 to 0.881, highlighting a reduced capacity to detect true-positive landslide events in the absence of hydrological data.  
Introducing rainfall into the model not only improved sensitivity but also enhanced model balance, as indicated by elevated F1 scores. RF achieved the best performance among all models with rainfall features, attaining an accuracy of 0.879, a recall of 0.909, and an F1 score of 0.885. XGBoost also experienced performance gains, particularly in precision and F1 score, underscoring the value of rainfall as a contributing factor in ensemble learning models. While Logistic Regression remained the lowest performant of the three, it too benefited from rainfall input, with notable gains in accuracy, recall, and ROC AUC.  
Figure 15 provides a comparative visual summary of model behavior through a radar chart illustrating five core metrics: accuracy, precision, recall, F1 score, and ROC AUC. The inclusion of rainfall predictors results in a visibly broader radar footprint, particularly for RF and XGBoost models, reflecting more balanced and improved performance across all metrics. The RF model demonstrates the most consistent and superior performance, followed closely by XGBoost. Even LR, though conservative in its predictions, shows measurable improvement, reinforcing the hydrological relevance of rainfall in post-seismic slope stability.  
These findings support the incorporation of hydro-meteorological variables into susceptibility models, particularly in earthquake-prone regions where precipitation can act as a secondary but powerful landslide trigger. The enhancement of classification performance upon the inclusion of rainfall data supports the hypothesis that rainfall, especially in the months immediately following a seismic event, makes a significant contribution to the geotechnical destabilization of slopes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **ROC AUC** \\ \hline \multicolumn{6}{c}{With Rainfall Features} \\ RF & 0.879 & 0.862 & 0.909 & 0.885 & 0.94 \\ XGBoost & 0.867 & 0.852 & 0.896 & 0.874 & 0.93 \\ LR & 0.800 & 0.792 & 0.826 & 0.809 & 0.87 \\ \multicolumn{6}{c}{Without Rainfall Features} \\ RF & 0.862 & 0.854 & 0.881 & 0.867 & 0.93 \\ XGBoost & 0.856 & 0.840 & 0.888 & 0.864 & 0.92 \\ LR & 0.775 & 0.747 & 0.847 & 0.794 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance with and without rainfall features.