# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Results of Landslide Prediction Based on Conventional Models
Core limitations of conventional modeling approaches:
* Spatiotemporal feature integration deficit: No existing implementations systematically integrate multidimensional predictors (terrain rasters and rainfall sequences), failing to capture dynamic interlocking mechanisms governing landslide triggering in geological processes. * Class imbalance vulnerability: Consistently sub-85% recall rates expose systemic bias toward conservative prediction paradigms prioritizing specificity over sensitivity, compromising early-warning efficacy through underdetection risks. * These findings collectively reveal inherent performance constraints in traditional landslide prediction frameworks, necessitating architectural innovation with dynamic feature recalibration mechanisms for enhancing operational viability through holistic framework redesign. While deep learning models like CNN often demonstrate superior capability in complex domains such as image recognition or sequential data modeling, its performance relative to the Random Forest (RF) model in this specific task warrants clarification, particularly given the introductory premise of deep learning's potential advantages. The observed underperformance of the standalone CNN model (Accuracy: 88.14%, F1-Score: 0.7090) compared to RF (Accuracy: 88.88%, F1-Score: 0.8655) can be primarily attributed to the nature of our input data and the core operational principles of each model. Crucially, our landslide prediction task utilizes tabular data (Excel format), where each sample is represented by a fixed set of 16 distinct geospatial and climatic features per location point. This structure differs fundamentally from the dense, spatially correlated grids (e.g., images, rasters) where CNNs excel through local feature extraction via convolutional filters. Applying CNNs directly to tabular data, where explicit spatial locality between adjacent features is often absent or less meaningful, can be suboptimal. The convolutional operations may struggle to capture the complex, potentially non-local interactions between heterogeneous features (e.g., slope angle vs. antecedent rainfall) as effectively as RF's ensemble of decision trees, which inherently perform robust feature selection and interaction modeling. Furthermore, RF's bagging mechanism inherently mitigates overfitting, which can be a challenge for CNNs, especially with datasets of limited size relative to model complexity (as is common in geospatial applications). The CNN's significantly lower F1-score starkly illustrates its difficulty in achieving a balanced trade-off between precision and recall on this tabular classification task under class imbalance. This highlights a key nuance: different deep learning architectures are specialized for distinct data modalities and tasks. While CNNs dominate spatially structured data, their direct application to conventional tabular feature vectors might not always yield benefits over well-tuned ensemble methods like RF for that specific data type. This limitation underscores the rationale behind our proposed CNN-LSTM-SAM-Attention model, which strategically fuses deep learning components (CNN for potential spatial patterns within derived representations, LSTM for temporal sequences) and attention mechanisms to transcend the limitations of both standalone classical models and monolithic deep learning architectures applied to heterogeneous data.