# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis
### Results and Analysis
#### 4.2.2 Classification Performance  
The confusion matrices for the training data and test data are shown in Figure 7. The prediction accuracy of the training set reaches 93.35%, while the prediction accuracy of the test set is 91.66%. This observation is further corroborated by the training curves (Figure 6), which show close convergence and synchronized behavior between the training and validation sets for both accuracy and loss metrics, confirming the absence of overfitting.  
Figure 6: Accuracy and loss curves during training of the CNN-LSTM-SAM-Attention model.  
Comparison of key indicators:  
As this study focuses on a binary classification task for landslide prediction, the following metrics are calculated:  
**Accuracy:** The proportion of correctly predicted samples (both positive and negative classes) relative to the total number of samples. This metric provides an intuitive measure of the model's overall predictive capability.  
**Precision:** The ratio of true positive samples among all samples predicted as positive (i.e., \"the accuracy of landslide predictions\"). It reflects the model's ability to \"reduce false alarms.\" A high precision indicates high reliability in the model's landslide predictions.  
**Recall:** The ratio of true positive samples correctly predicted as positive among all actual positive samples (i.e., \"the coverage of detected landslides\"). It evaluates the model's capability to \"reduce missed detections.\" A high recall suggests that the model is more likely to capture potential landslide risks.  
**F1-Score:** The harmonic mean of precision and recall, which balances the trade-off between these two metrics. It provides a comprehensive evaluation of the model's performance on imbalanced datasets, avoiding the limitations of relying on a single metric. In landslide prediction, a high F1-score indicates that the model achieves a better balance between reducing false alarms and missed detections, making it one of the core optimization objectives.  
The landslide prediction model proposed in this study demonstrates exceptional performance on both the training and testing sets. A detailed comparison of key metrics is presented in Table 2. The model's performance on the testing set compared to the training set highlights the following advantages:  
* Superior Generalization Capability: The testing set accuracy (91.66%) shows only a minor discrepancy (<2%) from the training set accuracy (93.35%), indicating no overfitting and confirming the model's applicability to real-world scenarios.
* Balanced Precision and Coverage: On the testing set, the model achieves high precision (92.73%) and recall (87.64%), with an F1-score of 0.9012. This reflects an effective trade-off between reducing false alarms and missed detections, aligning with disaster prevention requirements.
* Robustness to Class Imbalance: Despite the high proportion of negative class samples, the model stably captures nearly 90% of landslide events (recall: 87.64%), validating its adaptability to skewed data distributions.  
Figure 7: Confusion matrices for training set and test set.  
In summary, the proposed model exhibits superior comprehensive performance (F1 > 0.9), combining reliability, generalizability, and practicality. It provides a highly credible solution for landslide early warning systems, effectively addressing critical challenges in disaster risk management.