## 2 Literature Review
### Data-Driven Artificial Intelligence Methods
developed a dataset of roof displacement and used an artificial neural network (ANN) to predict the stability of longwal roadways based on roof displacement. The model achieved a goodness of fit of 0.911 [18]. Similarly, [PERSON] et al. used machine-learning techniques such as random forest and multilayer perceptron (MLP) to predict induced stress in underground coal mines [19]. [PERSON] et al. developed a convolutional neural network (CNN) using binary classification images to autonomously detect hazardous roof conditions [20]. Subsequently, they proposed a predictive model for roof condition classification based on transfer learning, achieving an accuracy of 86.4%. Notably, the study utilized an interpretability technique called integrated gradients to identify significant geological features in each image, demonstrating the potential to supplement human expert knowledge while also reducing experts' exposure to hazardous conditions. In the field of exploration and mining engineering, machine-learning methods have also demonstrated significant advantages in predicting other key parameters. [PERSON] et al. integrated multi-source data and used machine-learning models to analyze factors affecting mining damage, predicting the maximum height of fracture zones with a backpropagation (BP) neural network model [21]. This approach helped identify key factors that most significantly impact mine structure safety. [PERSON] et al. demonstrated the application of machine-learning algorithms in underground excavation. They used artificial neural networks and decision tree models to predict the risk of rock bursts in underground hard coal mines [22]. Their study emphasized the importance of machine-learning methods in identifying high-risk areas and preventing potential hazards, demonstrating that these methods are highly effective in practical applications and can effectively manage complex and dynamic underground environments. [PERSON] et al. showcased the application of machine-learning techniques in real-time seismic wave velocity prediction [23]. Through machine-learning models, they were able to accurately predict seismic wave velocities under underground mining conditions, which is crucial for enhancing the real-time monitoring and accuracy of mine safety. [PERSON] and [PERSON] used an improved cuckoo search algorithm (ICS) combined with extreme learning machine (ELM) to construct a model for predicting the height of fractured water-conducting zones [24]. This model showed significant advantages in handling nonlinear and high-dimensional data, and it outperformed traditional methods in prediction accuracy. Although machine-learning methods have been applied to predict roof stress in mining areas, current studies still face three significant limitations. First, they rely on geological and engineering parameters recorded by various instruments but overlook the increasingly available image data [25]. In fact, with advancements in computer vision technologies, image data have been widely used in fields such as medical diagnostics, autonomous driving, and cultural heritage preservation, and are even regarded as an important research resource [26; 27; 28]. Second, due to the limitations of the predictor variables, the goodness of fit and prediction accuracy of existing studies still need improvement, with the R\({}^{2}\) values of most studies being below 0.8, which also weakens the practical usability of these models [29; 30; 31]. Third, prior research has primarily focused on improving prediction accuracy by optimizing models and adjusting parameters, but has relatively ignored model interpretability. This has not only drawn skepticism and criticism from domain experts but also hindered the practical application of the models [32].