# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Comparative Analysis of the CNN-LSTM-SAM-Attention Model and Traditional Models  
The key metrics of the CNN-LSTM-SAM-Attention model compared with traditional models are shown in Table 3, and the following Figure 9 compares the PR curve and ROC curve graphs of each model. According to the comparison, the CNN-LSTM-SAM-Attention model put forth in this study outperforms the traditional models in the landslide prediction task by a considerable margin. Its strong discriminant in high-dimensional feature spaces is confirmed by the fact that both ROC-AUC (0.9703) and PR-AUC (0.9740) are substantially higher than the conventional models and near the theoretical maximum (1.0) capability. Beginning with the fundamental metrics, including accuracy, recall, F1 value, AUC value of PR curve, and ROC curve, we compare the CNN-LSTM-SAM-Attention model's performance to that of conventional models. The analysis is as follows:  
**Accuracy:** The CNN-LSTM-SAM-Attention framework achieves superior accuracy (91.66%), outperforming all conventional models by 2.78-10.59 percentage points, with a 2.78% improvement over the suboptimal Random Forest (88.88%). While Random Forest (88.88%) and CNN (88.14%) demonstrate competency in static spatial feature extraction, their exclusion of temporal dynamics critically compromises adaptability to evolving landslide conditions. Similarly, SVM (87.35%) and logistic regression (81.07%) exhibit fundamental constraints in modeling complex nonlinear interactions due to inherent architectural limitations. This performance hierarchy conclusively validates the proposed model's enhanced predictive capacity through spatiotemporal fusion (CNN-LSTM) and adaptive attention mechanisms (SAM). **Recall:** 87.64% for CNN-LSTM-SAM-Attention, a 5.13% improvement over the optimal traditional model (Random Forest, 82.51%), covering more real landslide events. Logistic regression (70.90%) is only applicable to linearly divisible scenarios due to the simplicity of the model, and the risk of underreporting is extremely high.SVM (79.58%) and CNN (84.94%) do not explicitly enhance the ortho-classical sample response, although they partially alleviate the problem through nonlinear modeling. It can be seen that the SAM module of the model in this paper significantly reduces underreporting by dynamically adjusting the feature weights. **F1-score:** The CNN-LSTM-SAM-Attention model achieves a superior F1-score of 0.9012, outperforming the best traditional model (Random Forest: 0.8655) by 4.1 percentage points. This demonstrates enhanced precision-recall balance (92.73% precision vs. 87.64% recall) compared to conventional approaches. Traditional models exhibit limitations: Random Forest (0.8655 F1) and SVM (0.8452 F1) show inadequate coordination between false alarms and missed detections due to unmodeled temporal dependencies, while CNN's single spatial feature extraction results in substantially lower performance (0.709 F1). Our model's high F1-score (>0.9) confirms its effectiveness in maintaining safety-economic equilibrium for disaster prevention applications. Figure 9: Comparison of PR and ROC curves across multiple models. **PR-AUC:** The CNN-LSTM-SAM-Attention model achieves a superior PR-AUC of 0.9740, substantially exceeding conventional methods (logistic regression: 0.8885; SVM: 0.9487). Notably, our solution maintains precision >90% in high-recall regimes (Recall > 0.8) (Figure 8), demonstrating effective coverage of landslide events with minimal false positives while preserving operational reliability. **ROC-AUC:** The CNN-LSTM-SAM-Attention model achieves a near-optimal ROC-AUC of 0.9703, significantly outperforming conventional approaches (e.g., logistic regression: 0.8809; SVM: 0.9453). While Random Forest (0.9535) and CNN (0.9520) demonstrate relatively strong performance, their inability to model temporal features results in compromised dynamic event discrimination.