# Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches
## 3 Results and Discussion
### Training of the Predictive Model for Pressure Exertion Characteristic Parameters  
To ensure a sufficient dataset, the model training in the third step was based on data collected from nine working faces. Using the method for solving pressure exertion characteristics from historical data described in Section 2.2, a total of 92 pressure exertions were identified, with the statistics summarized in Table 2. Among these 92 pressure exertions, some did not include all three complete stages of pressure exertion. Therefore, only those with all three stages were used to establish the dataset. Since, in actual prediction, the data for each stage of pressure exertion increases with the number of support cycles, the dataset gains an additional sample for each additional support cycle in the pressure exertion stage. The number of samples for a single pressure exertion corresponds to the number of support cycles it contains. When dividing the dataset, a random number was set to split it into a training set accounting for 80% of the total pressure exertions and a test set accounting for 20%, resulting in a training set of 2363 samples and a test set of 549 samples. In the network shown in Figure 5, the support resistance data from 12 measurement lines within one support cycle constitutes the input data for a single timestamp. The number of support cycles contained in each stage of pressure exertion varies. According to statistics, the dataset samples contain a maximum of 23 support cycles in the stable stage, 57 in the unstable movement stage, and 236 in the pressure exertion stage. To ensure sufficient scalability of the model structure, the number of timestamps is set to 32 for the stable stage, 54 for the unstable movement stage, and 256 for the pressure exertion stage. When the number of support cycles is less than the number of timestamps, zero padding is used. To analyze the impact of missing pressure exertion stages on prediction results and enable the prediction of pressure exertion characteristic parameters for cases lacking all three complete stages, the model was trained with four different input data scenarios: the pressure exertion stage alone, the unstable movement stage plus the pressure exertion stage, the stable stage plus the unstable movement stage plus the pressure exertion stage, and the unstable movement stage alone. The model was trained separately for each of these scenarios. During the training process, the prediction accuracy for both the training set and the test set was monitored, and the optimal prediction results for the test set under each scenario were recorded. These results are summarized in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Workface ID** & **Pressure Exertion Location** & **Number of** \\  & **(Cycle ID)** & **Pressure Exertions** \\ \hline
8100 & 300, 319, 358, 409, 473 & 5 \\
8103 & 1902, 1962 & 2 \\  & 224, 245, 259, 329, 341, 406, 472, 489, \\  & 521, 597, 648, 687, 695, 763, 801, 810, \\
8104 & 930, 952, 1283, 1361, 1402, 1415, 1557, 1588, \\  & 1608, 1700, 1742, 1761, 1797, 1823, 1972, 2043, \\  & 2082, 2119 \\
8105 & 899 & 1 \\
8106 & 234, 292, 340, 434, 471, 515, 539 & 7 \\
8107 & 1426, 1459, 1537, 1600, 1621, 1625, 1662, 1737 & 8 \\  & 43, 90, 124, 171, 188, 194, 283, 297, \\  & 371, 412, 451, 512, 549, 588, 653, 673, \\
8113 & 686, 746, 854, 989, 1035, 1054, 1097, 1107, \\  & 1115, 1128, 1166, 1232, 1497, 1538, 1608, 1636, \\  & 1642, 1661, 1705 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the observed pressure exertion situations. As can be seen from Table 3, when the input information includes three complete stages, the prediction accuracy for all four pressure exertion characteristic parameters is the highest, making it the optimal model.