# Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine
## 2 Development of Prediction Models for Coal Seam Gas Content
### Application of the Model in Engineering and Evaluation of Its Predictive Performance  
The developed model was successfully applied for on-site prediction of gas content in the 15 th mining area of the Jiulishan Coal Mine in Jiaozuo, Henan. However, it should be noted that the model was developed based on the gas content and related influencing factor data specific to the 15 th mining area. This is because different geological units have their own gas geological laws, and the main factors influencing gas content can vary greatly among different mining areas, zones, seams, and mines. Therefore, if the model needs to be applied in a different location, then it is necessary to reanalyze local gas content influencing factors and collect relevant data to retrain the model with new information. Before applying the gas content prediction model on site in the 15 th mining area, it is essential to collect gas content influencing factor data from the measurement point. All influencing factors should be carefully recorded, and five sample datasets, as shown in Table 6, could be used as a reference.  
Upon inputting the data of the five influencing factors of gas content, as listed in Table 6, into the trained model for prediction, the resulting predictions were analyzed for the five samples when they were all in the test set during 10 simulations. The mean predicted results of the three models for the on-site gas content prediction application process are presented in Figure 15.  
Based on the results presented in Figure 15, it can be observed that the GASA-KELM model exhibits superior performance in predicting gas content in coal seams, with a maximum relative error of 16.59% and a minimum relative error of 7.93%. The average relative and absolute errors for the prediction are 10.6% and 2.28%, respectively. In comparison, the GASA-SVM model achieves a maximum relative error of 19.89% and a minimum relative error of 9.01%, with average relative and absolute errors of 13.04% and 2.73%, respectively. Meanwhile, the GASA-BPNN model yields a maximum relative error of 20.64% and a minimum relative error of 10.14%, with average relative and absolute errors of 14.31% and 3.18%, respectively. Taking into account both the relative and absolute errors in the prediction, it can be inferred that the GASA-KELM model is more effective in generalizing to new sample data and provides higher accuracy and stability in predicting gas content in new data samples. Thus, it is better suited to meet the goals and requirements for predicting gas content. The GASA-SVM model performs comparably to the GASA-KELM model in terms of accuracy and generalization ability, while the GASA-BPNN model exhibits relatively lower accuracy than the other two models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample Number** & \(\mathbf{X_{0}}\)**/**(m\({}^{3}\).t\({}^{-1}\))** & \(\mathbf{X_{1}}\)**/**(m)** & \(\mathbf{X_{2}}\)**/**(m)** & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\)**/**(m)** & \(\mathbf{X_{5}}\) & \(\mathbf{X_{6}}\) & \(\mathbf{X_{7}}\) & \(\mathbf{X_{8}}\)**/(m)** \\ \hline
1 & 24.03 & 449 & 4.75 & 0.0107 & 199.44 & 0.3299 & 0.1264 & 0.0027 & \(-\)364 \\
2 & 17.65 & 402 & 5.48 & 0.0285 & 183.86 & 0.3810 & 0.0298 & 0.0199 & \(-\)308.6 \\
3 & 24.22 & 305.1 & 5.39 & 0.0291 & 135.74 & 0.4302 & 0.0895 & 0.0615 & \(-\)218.9 \\
4 & 19.71 & 481 & 6.24 & 0.0166 & 118.10 & 0.5123 & 0.0135 & 0.0047 & \(-\)394.2 \\
5 & 22.98 & 387.8 & 5.55 & 0.0243 & 98.24 & 0.5808 & 0.0494 & 0.0012 & \(-\)295.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Field application sample data sheet.