# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion  
### Results of Landslide Prediction Based on Conventional Models  
In order to verify the optimization performance of the CNN-LSTM-SAM-Attention model in the landslide prediction task, this study compares it with traditional models such as Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and CNN. All models use the same training set (2020 landslide data from Kumamoto Prefecture, Japan), feature inputs, and evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC, and PR-AUC) to ensure comparable results. The performance metrics of the Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Convolutional Neural Network (CNN) models are summarized in Table 3:  
The logistic regression (LR) model demonstrates substantially lower predictive accuracy (81.07%) compared to other models, suggesting that its linear assumption struggles to capture the nonlinear interactions among landslide-contributing factors such as slope gradient and rainfall patterns. With the lowest recall rate (70.90%) among conventional models, this approach exhibits critical deficiencies in identifying positive-class instances (landslides), potentially leading to systematic underreporting of disaster risks. The model's comprehensive performance metrics further reveal limitations: both the F1-score (0.7624) and ROC-AUC (0.8809) remain suboptimal, particularly when handling datasets with moderate class imbalance (60% negative-class instances). The support vector machine (SVM) achieves the highest precision (90.12%) among conventional models, demonstrating its enhanced capacity to process nonlinear features through kernel function optimization compared to logistic regression. Nevertheless, while its recall rate (79.58%) exceeds that of logistic regression, a persistent 20.42% detection gap persists, fundamentally compromising its suitability for high-reliability early-warning systems requiring near-complete landslide identification. The random forest (RF) model demonstrates superior comprehensive performance with accuracy (88.88%), ROC-AUC (0.9535), and PR-AUC (0.8917) outperforming both SVM and logistic regression, confirming its ensemble learning mechanism effectively captures complex nonlinear relationships. A critical limitation persists: the F1-score (0.8655) reveals systematic deficiencies in precision-recall harmonization (91.01% vs. 82.51%), likely attributable to inherent decision tree overfitting tendencies or feature importance distribution imbalances within the ensemble architecture. The convolutional neural network (CNN) demonstrates superior performance over logistic regression and SVM in both accuracy (88.14%) and recall (84.94%), confirming its efficacy in capturing localized spatial patterns within geospatial data. While achieving precision (87.37%) and ROC-AUC (0.952) comparable to Random Forest, the model's F1-score (0.709) remains substantially lower than its counterparts, revealing systemic deficiencies in optimizing precision-recall equilibrium. Regarding class imbalance sensitivity, despite attaining an elevated PR-AUC (0.9562), the recall rate (84.94%) underperforms the integrated \begin{table}
\begin{tabular}{l l l l l l} \hline  & **LR** & **SVM** & **RF** & **CNN** & **CLSA \({}^{*}\)** \\ \hline Accuracy & 81.07\% & 87.35\% & 88.88\% & 88.14\% & 91.66\% \\ Precision & 82.45\% & 90.12\% & 91.01\% & 87.37\% & 92.73\% \\ Recall & 70.90\% & 79.58\% & 82.51\% & 84.94\% & 87.64\% \\ F1-Score & 0.7624 & 0.8452 & 0.8655 & 0.7090 & 0.9012 \\ PR-AUC & 0.8809 & 0.9453 & 0.9535 & 0.9520 & 0.9703 \\ ROC-AUC & 0.8885 & 0.9487 & 0.8917 & 0.9562 & 0.9740 \\ \hline \multicolumn{6}{l}{\({}^{*}\)CLSA denotes the CNN-LSTM-SAM-Attention model.} \\ \end{tabular}
\end{table} 
Table 3: Performance comparison of different models. CNN-LSTM-SAM framework (87.64%) proposed in this work, highlighting the constrained capacity of standalone CNN architectures to sufficiently represent positive-class instances.