## 4 Discussion
### Model Interpretation and Technical Limitations  
This study compares six traditional machine learning models for landslide prediction in terms of a binary classification problem: whether a landslide occurs or not. The XGB model was selected based on its good performance, and its confusion matrix shows that it produced 556 TPs, 73 FPs, 63 FNs, and 547 TNs. This indicates that the model correctly identified 556 landslide events and successfully excluded 547 non-landslide areas. However, the model incorrectly classified 63 non-landslide points as landslide points and 73 landslide points as non-landslide points. Overall, the model performs well in landslide detection tasks, exhibiting high accuracy and strong discriminative ability.  
By contrast, the KNN and MLP models showed weaker performance, there are several potential reasons that might explain the suboptimal performance of the KNN and MLP models. For the KNN model, although increasing the value of k generally reduces model complexity, its performance still lags behind that of decision trees. This may be due to the high dimensionality of the features or the unclear classification boundary at the 0.5 probability threshold between the landslide and non-landslide classes. As a result, the model struggles to form stable and representative neighborhoods in a high-dimensional space [85; 86]. Increasing k can therefore introduce more noisy samples rather than meaningful neighbors, further degrading generalization [87]. As for the MLP model, the loss function converged during training, and the performance difference between the training and validation sets was minimal, indicating no clear overfitting or underfitting. However, overall predictive accuracy remained relatively low, possibly due to several factors: a limited training sample size inadequate for the effective generalization of deep networks and the limited expressive power of input features failing to provide sufficient discriminatory information [88; 89]. Future work could focus on improving feature engineering, expanding the training dataset, or optimizing the network structure to enhance model performance.  
The selection of analysis units plays a critical role in landslide susceptibility modeling. To better represent spatial heterogeneity, some studies have adopted geomorphology-based units, such as sub-watersheds or slope segments, which reflect terrain-driven spatial processes and help to reveal the geomorphic controls and distribution patterns of landslides. In this study, a regular grid with a resolution of 1 km\({}^{2}\) was used as the basic analysis unit. This approach offers two key advantages: it enables good spatial resolution compatibility with multi-source datasets such as climate, soil, topography, and vegetation, thereby facilitating data integration and regional-scale modeling; the regular grid structure provides a uniform spatial framework, which is beneficial for conducting consistent analyses and risk comparisons across large areas.  
For the return period calculations, we selected the maximum value of the moving sequence of the EAP for each meteorological station and year for the fitting of the return period. Although this reflects the precipitation trends under extreme precipitation scenarios, it still has certain limitations. The moving sequence's maximum value only considers extreme values within a single time window and does not fully capture precipitation variations over longer timescales, leading to an increase in the landslide susceptibility probability at different return periods. Furthermore, the spatial heterogeneity of precipitation and climate differences across different regions may affect the accuracy of the return period calculation. Therefore, future studies could introduce more complex spatiotemporal analysis methods by incorporating additional meteorological factors and regional climate characteristics to improve the accuracy of precipitation return period assessments.