# Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches
## 1 Introduction
[12] addressed the issue of abnormal pressure in isolated working faces of thin coal seams beneath residual coal pillars using a combination of theoretical analysis, numerical simulation, and field measurements. The study focused on the stress distribution pattern in the 33,107 isolated working faces. [PERSON] et al. [13] analyzed the characteristics of roof caving and overburden fracture development in Xinjiang using physically similar simulation methods. Field monitoring revealed that thehydraulic support pressure was relatively low in shallow coal seams with thin bedrock, but the stability of the coal wall and roadways was good. [PERSON] et al. [14] proposed an \"umbrella arch\" overburden structure for isolated working faces with hard roofs and derived an expression for the initial supporting force of the supports under these conditions. This theoretically established the correspondence between the near-gob area and the isolated working face. Numerical simulations were used to understand the variation in peak elastic energy difference on the solid coal side of the transport and extraction roadways in irregular island-shaped working faces. In summary, previous methods mainly focused on studying overburden characteristics and ground pressure laws and had limited accuracy in predicting incoming pressure. This was primarily due to difficulties in accurately extracting rock mass physicochemical parameters and modeling geological structures. Given the current research progress in extracting rock mass physicochemical parameters and geological structure modeling, it is extremely challenging to improve the accuracy of pressure prediction by solving these issues. The development of big data and artificial intelligence technologies has led to the emergence of data-driven research methods. These methods focus on data, utilizing AI technologies to deeply analyze and mine big data, uncovering potential laws and patterns within the data to guide scientific research, product development, or decision-making. This approach only requires inputting existing information and, based on the learned correlation between existing information and the information to be predicted, it can generate prediction results, thereby bypassing the processes of extracting rock mass physicochemical parameters and geological structure modeling. Furthermore, since data are objective and not influenced by researchers' subjective factors, predictions driven by data are more objective and accurate. According to surveys, data-driven research has already been conducted in fields such as real-time prediction of shield tunneling machine posture and position [15], drug design [16], metal additive manufacturing [17], PM2.5 prediction [18], monthly traffic flow prediction [19], mechanical fatigue life prediction [20], and landslide prediction [21]. This has become a current research trend, aligning with the national call for deep integration of AI into various industries. Data-driven prediction of pressure exertion in working faces has been explored in previous studies, primarily focusing on the prediction of support resistance, which is the foundation for predicting pressure exertion. Building on these works, this paper will systematically investigate data-driven methods for predicting pressure exertion in working faces, breaking through the bottleneck of improving prediction accuracy under existing research paradigms. It aims to provide relevant methods and theories for coal mine safety mining technologies in the era of big data and artificial intelligence.