## 5 Conclusions  
The experimental results demonstrate that image features outperform traditional geological parameters in predictive performance. Models trained on integrated data significantly surpass those based solely on geological parameters in terms of interpretability, complexity, and predictive power. This finding suggests that image features can serve as a new and effective resource for prediction, greatly enriching the research methods and applications in the field of stress prediction. Consequently, we recommend that mining engineering projects conducted in complex geological environments should place a high importance on the collection and processing of image data, thereby providing reliable technical support for the safety management of mining operations. To better understand the internal mechanisms of the models, this study introduced SHapley Additive exPlanations (SHAP) for model interpretation. This method, being applied for the first time in this research area, aids researchers in uncovering new domain knowledge from multi-source data. The combination of SHAP analysis and correlation heatmaps revealed the complementary relationship between image features and traditional geological parameters, as well as the more complex mechanisms underlying stress distribution. Unlike previous studies that mainly focus on enhancing predictive performance through the optimization or integration of various machine-learning models, this study emphasizes the expansion of data resources. By integrating image features with traditional geological parameters, it achieves a good balance between prediction accuracy and interpretability. The approaches of prior studies often lead to overly complex and difficult-to-interpret models, which hinder their practicality and widespread adoption in real-world applications. In contrast, the integration of image features with traditional geological parameters in this study enables the model to understand and predict the stress distribution  
Figure 9: The beeswarm plot for the random forest (RF) model. from multiple dimensions. This not only enhances the model's predictive ability but also provides a more comprehensive explanatory framework for stress prediction. Unlike previous studies that mainly focused on improving prediction performance by optimizing or integrating various machine-learning models, the primary innovation of this research lies in the introduction of image data to expand the set of predictors, followed by the use of the SHAP method to achieve a good balance between prediction accuracy and interpretability. It is worth noting that this study, on the one hand, improved the predictive ability of the model, confirming the applicability of the approach of using deep learning to extract image features in this field; on the other hand, through knowledge mining, it discovered many feature relationships and interactions, providing a more comprehensive explanatory framework for stress prediction. Interestingly, the introduction of a new predictor--image data--not only serves as a means for verifying or falsifying existing theories but also acts as a source of new hypotheses and discoveries. Looking ahead, stress prediction research can be further optimized in several directions with advancements in big data, artificial intelligence, and related fields. First, future research can explore more types of data sources. Although this study successfully combined 3D laser scanning image features with traditional geological parameters, other forms of spatial information, such as remote sensing data, ground-penetrating radar data, and seismic wave data, have not yet been considered. Second, the further optimization of model interpretability and deepening of knowledge extraction is necessary.