# Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches
## 4 Conclusions  
Under the current technological revolution spearheaded by artificial intelligence, data-driven intelligent strata control has emerged as a research frontier in safety hazard prevention for underground coal mining. This paper investigates data-driven prediction methodologies for advanced pressure exertion locations and their impact severity. The \begin{table}
\begin{tabular}{c c c c c c c} \hline
**True Type**Predicted Type** & **Small** & **Large** & **Strong** & **Recall** & **Precision** & **F1-Score** \\ \hline Small Pressure & 2509 & 112 & 27 & 0.95 & 0.93 & 0.94 \\ Large Pressure & 146 & 938 & 54 & 0.82 & 0.88 & 0.85 \\ Strong Pressure & 53 & 16 & 1091 & 0.94 & 0.93 & 0.94 \\ \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix and classification metrics for the pressure exertion types. Figure 14: Distribution of prediction errors for the maximum resistance during pressure exertion across all samples. Figure 15: Distribution of prediction errors for the number of cycles during pressure exertion across all samples. predictive outcomes will generate early warnings regarding the necessity of implementing prevention and control measures for potential roof disasters, while the formulation of more scientifically grounded control strategies constitutes a critical focus for subsequent research. The principal conclusions derived from this study are as follows:  
1. A data-driven method for predicting pressure exertion in coal mine working faces has been proposed, which defines the content of such predictions. The prediction process is divided into three steps: the first step is to predict the support resistance data ahead of the working face; the second step is to classify the pressure labels of the coordinate units; and the third step is to predict the characteristic parameters of pressure exertion. 2. A method for solving the characteristics of pressure exertion based on historical data is proposed, and a three-step prediction model for pressure exertion is designed and discussed. In the first step, a deep spatiotemporal sequence model is selected for support resistance prediction. In the second step, an image segmentation-based classification model is chosen for pressure label classification. In the third step, a fusion model consisting of three LSTM networks is designed. 3. The model was trained using data from 10 working faces in the Datong mining area. The training set error for the first step was 1.83 kN, and the test set error was 4.65 kN. The training set accuracy for the second step was 99.38%, and the test set accuracy was 97.77%. For the third step, the dynamic pressure coefficient error was 0.17, the maximum resistance error during pressure exertion was 810.93 kN, the error in the number of cycles during pressure exertion was 9.96 cycles, and the accuracy for pressure exertion type classification was 92.35%. 4. To simulate the actual conditions of application scenarios, the input data for the second and third steps were set as the output data from the previous step, and the model was evaluated. The model's mean absolute error for support resistance prediction was 1035.21 kN, and its classification accuracy for coordinate unit pressure labels was 82.90%, with a cycle pressure label accuracy of 90.93%. In the simulated scenario, there were 9922 actual instances of pressure exertion, and the model predicted 10336 instances, with 9046 of these predictions corresponding to actual instances of pressure exertion. An evaluation was conducted of the feature parameter predictions for 4946 instances of pressure exertion that included three complete pressure exertion phases. The mean absolute error for the dynamic pressure coefficient was 0.21, the maximum resistance during pressure exertion was 1218.31 kN, the number of cycles during pressure exertion was 11.03 cycles, and the classification accuracy for pressure exertion types was 91.75%. Y.C. : Methodology, Validation, Writing--original draft, Writing--review and editing. C.L.