# Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine
## 4 Conclusions  
The main conclusions are as follows:  
1. After conducting verification, Rastrigin's function was optimized 20 times using the PSO, GA, SA, and GASA algorithms under the same conditions. The algorithms completed the iterative optimization at the 102 nd, 156 th, 176 th, and 78 th iterations. The average optimization values of the four algorithms were 9.2472 \(\times\) 10\({}^{-4}\), 7.9003 \(\times\) 10\({}^{-3}\), 9.1873 \(\times\) 10\({}^{-2}\), and 5.6935 \(\times\) 10\({}^{-4}\), with respective variances of 3.1547, 3.7519, 7.6823, and 2.0524. After considering the average optimization results over 20 iterations, the variance of the optimization results, and the average number of iterations, the GASA designed in this paper exhibits stronger capabilities in optimizing complex functions and providing stable global search performance compared to the PSO, GA, and SA algorithms. Furthermore, the GASA algorithm demonstrates a more efficient optimization speed and higher optimization accuracy for complex functions compared to single algorithms, effectively avoiding the issue of optimization algorithms being prone to local optima.
2. In the process of constructing the GASA-BPNN prediction model, the GASA failed to meet the target requirements within 800 iterations. Conversely, during the construction of the GASA-SVM and GASA-KELM gas content prediction models, the GASA was able to discover the optimal initial parameters during the 673 rd and 487 th iterations, respectively. This disparity can be attributed to the fact that the number of parameters to be optimized in BPNN is significantly greater than in SVM and KELM. As a result, the optimization process for the GASA-SVM and GASA-KELM models was much faster and produced higher-quality results than the BPNN model.
3. During 10-fold cross-validation, the GASA-BPNN, GASA-SVM and GASA-KELM models yielded average relative errors of 15.74%, 13.85%, and 9.87%, respectively. The  
Figure 15: Predicted results of the model field application.  
corresponding variances of the 10 cross-validation results were 3.99, 2.76 and 2.05. Notably, in comparison with the GASA-SVM and GASA-BPNN models, the GASA-KELM model displayed superior accuracy and stability in predicting gas content. Subsequently, the GASA-KELM model was tested on twelve additional samples, which further revealed the model's exceptional performance in terms of prediction accuracy and generalization ability to new sample data.
4. The developed GASA-KELM model proves to have significant advantages over other ANN models in terms of high accuracy in gas content prediction, stability in prediction, and strong generalization ability when applied to the gas content prediction case of the Juilishan Mine's 15-mining area. These advantages are essential for the accurate prediction of gas content and for formulating effective regional gas management strategies.  
Conceptualization, S.T. and L.M.; methodology, J.M. and L.M.; software, L.M.; validation, H.L.; formal analysis, L.M.; investigation, J.M.; resources, S.T. and F.T.; data curation, L.M.; writing--original draft preparation, L.M. and J.M.; writing--review and editing, S.T., F.T. and H.L.; visualization, J.M., F.T. and H.L.; supervision, S.T., F.T. and H.L.; project administration, S.T.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. U1904210, Grant No. 51874237, and Grant No. 71273208).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).  
The authors would like to thank the Jiulishan Mine of Henan Coking Coal Energy Co., Ltd.  
The authors declare no conflict of interest.