# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 6 Conclusions  
This study proposes the CNN-LSTM-SAM-Attention model for landslide susceptibility assessment, with systematic performance evaluation conducted using 2020 Kumamoto landslide event data against benchmark models (Random Forest, SVM, logistic regression). The principal findings are as follows:  
1. The proposed CNN-LSTM-SAM-Attention model demonstrates superior landslide prediction capabilities in real-world engineering applications. Trained on a rigorously curated dataset from the 2020 Kumamoto landslide (16-dimensional features, 19,899 samples after outlier removal), the model achieves exceptional metrics: 91.66%accuracy, 0.9012 F1-score, and 87.64% recall, indicating robust predictive performance with balanced false-positive/false-negative mitigation critical for disaster underreporting prevention. Near-optimal ROC-AUC (0.9703) and PR-AUC (0.9740) scores approaching theoretical maxima confirm its discriminative power in high-dimensional feature spaces and class-imbalanced scenarios.
2. The CNN-LSTM-SAM-Attention model significantly outperforms the four traditional models (random forest, SVM, logistic regression, and CNN), reflecting the effectiveness of spatio-temporal feature fusion and dynamic optimization. By using the same training set and evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC and PR-AUC), landslide prediction is carried out with four traditional models, namely, Random Forest, Support Vector Machine, Logistic Regression and CNN, and the results are compared with the model proposed in this paper. The comparison results show that the CNN-LSTM-SAM-Attention model outperforms the traditional models in all five indicators, which shows that the model in this paper effectively improves the discriminative ability of the model in high-dimensional feature space through the spatio-temporal feature fusion by the CNN and LSTM modules and the dynamic adjustment of the feature weights by the SAM module.
3. Practical application value: The high precision (92.73%) of the model can reduce the cost of misjudgment in landslide warning, while the high recall (87.64%) can cover most of the potential landslide events, providing high confidence support for local disaster prevention planning and emergency response. Compared with traditional models, its comprehensive performance is more adaptable to the prediction needs of complex terrain (e.g., steep slopes, broken rock formations) and dynamic climate (e.g., sudden rainfall in the rainy season).  
In conclusion, this study proposes a CNN-LSTM-SAM-Attention model with the background of actual landslide disasters, which has high accuracy and reliability in predicting the occurrence of landslides under the influence of multidimensional complex factors, and provides a practical reference for the deepening of the application of deep learning in the field of geohazards.  
Conceptualization, H.W., J.N. and Y.L.; methodology, H.W. and Y.L.; validation, H.W., J.N., Y.W. and D.Q.; formal analysis, Y.L. and Y.W.; investigation, H.W., Y.L. and D.Q.; resources, J.N. and D.Q.; data curation, Y.W. and Y.L.; writing--original draft preparation, H.W. and Y.L.; writing--review and editing, J.N. and D.Q.; visualization, Y.W.; supervision, J.N.; project administration, J.N.; funding acquisition, H.W. and J.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Research and Development Program of China Railway Group Limited (2022-Major Special Project-07), the National Natural Science Foundation of China (Grant No. 41772298), and the Natural Science Foundation of Shandong Province (Grant No. ZR2023 MD051).  
role in the design of the study; in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.