## 4 Results
### SHAP-Based Analysis and Its Practical Implications
#### 4.3.1 SHAP-Based Feature Importance Analysis  
When processing numerical data in the CNN-Transformer model, the features first go through the CNN's convolution and pooling operations to extract the local features, and then the global features are captured by the Transformer to capture the relationship between the global features; therefore, when SHAP is used to interpret the model, it acts directly on the deep feature layer, and the results reflect only the CNN-Transformer processed features' contribution, which cannot be directly related to the original input features. At this point, reverse engineering needs to be introduced to map the importance of the deep features back to the original input space by restoring the feature extraction process of the CNN. Specifically, reverse engineering requires layer-by-layer reverse operation for the CNN part: use transpose convolution to gradually restore the feature map extracted from the convolutional layer, use up-sampling to restore the dimensionality reduction operation after pooling, and finally, decode the feature contribution of each layer to the input feature space step by step. For the Transformer part, the global feature interactions are incorporated into the interpretation using the feature weights recorded by its attention mechanism. In this way, the importance of deep features can be reasonably redistributed to the original numerical features, combining global and local interpretations to completely reflect the model's prediction logic. This approach reflects both the local pattern-capturing ability of CNN and incorporates the global feature modeling of Transformer, which helps to explain more intuitively the complex model's dependence on the raw data and the basis for decision making. The analysis results are shown in Figure 9.  
The detailed analysis of feature importance not only enhances the interpretability of the model, but also provides strong support for data processing and model optimization in regression prediction tasks. The application of this method proves the applicability and effectiveness of SHAP in the interpretation of complex deep-learning models. Figure 10 shows a comparison of the degree of contribution of different influencing factors to the weir life, which is based on SHAP analysis and highlights the differences between the characteristic importance of different factors. In the process of predicting the lifespan of landslide dams, the importance of each feature exhibits significant variation. Figure 10 visually demonstrates the degree of influence of each feature on the prediction results through the height of the rectangles, accompanied by a quantitative representation. A taller rectangle indicates that the corresponding feature contributes more substantially to the prediction outcome, while a shorter rectangle suggestsa lesser influence. This visualization method clearly highlights the relative importance of different features within the model, offering valuable insights into the key drivers behind the prediction results. As can be seen in the figure, dam length is the most important feature in the model predictions, contributing 26.9% to the results, showing the critical influence of the weir length on its lifetime. Dam lake volume (DLV) is the next most important feature, contributing 20.7%, indicating that the amount of water that a weir can hold significantly affects its lifetime. Upstream catchment area and dam volume contribute 11.3% and 9.6%, respectively, to the model, showing the significant influence of hydrology and dam structure on weir life. Figure 10: Importance of influencing factors on longevity of landslide dam. Figure 9: SHAP analysis of various influencing factors. Dam height and dam width contribute 8.6% and 8.0% to the model, respectively, indicating that although they are not as important to the model as dam length and storage volume in terms of their predictive ability, they are still important structural parameters affecting longevity.