# Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion
## 4 Experimental Simulation
### Analysis of Correlation Degree of Risk Factors of Gas Outburst

Table 4: Classification of influencing factors based on correlation degree. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Name** & **Grey Correlation Degree Value** & **Rank** \\ \hline q & 0.727 & 5 \\ Relative abundance of methane & 0.709 & 6 \\ Numerical simulation of fault height & 0.699 & 7 \\ Absolute gas emission rate & 0.691 & 8 \\ Coal firmness coefficient & 0.678 & 9 \\ Ash content & 0.670 & 10 \\ Thickness of coal seam & 0.668 & 11 \\ Exploitation depth & 0.667 & 12 \\ Numerical simulation of gas pressure & 0.666 & 13 \\ K\({}_{1}\) & 0.649 & 14 \\ Initial gas pressure & 0.646 & 15 \\ Volatiles & 0.638 & 16 \\ Numerical simulation of ground stress & 0.635 & 17 \\ Gas adsorption constant a & 0.593 & 18 \\ Numerical simulation of stone gate thickness & 0.578 & 19 \\ Smax & 0.572 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._fitting degree of the models for each indicator group, the key controlling factor indicator group was determined. The comparative effects of each model are shown in Table 5.  
For the five models (XGBoost, GBDT, RF, SVM, and stacking), the table above presents metrics such as MAE, MSE, and R\({}^{2}\), which reflect the performance of the models. MAE and MSE represent the errors of the models, and a lower value indicates higher accuracy in the predictions. The R\({}^{2}\) coefficient represents the goodness of fit of the models. Within a certain range, a higher value indicates a better fit and a more effective prediction. Based on the data in the table:  
For the first set of indicators, the XGBoost predictive model yields an MAE, MSE, and R\({}^{2}\) of 0.091, 0.091, and 0.933, respectively. The SVM predictive model scores an MAE, MSE, and R\({}^{2}\) of 0.152, 0.152, and 0.888, respectively. The RF predictive model, on the other hand, performs with an MAE, MSE, and R\({}^{2}\) of 0.061, 0.061, and 0.955. As for the GBDT predictive model, it achieves an MAE, MSE, and R\({}^{2}\) of 0.061, 0.061, and 0.955. Lastly, the stacking predictive model exhibits an MAE, MSE, and R\({}^{2}\) of 0.061, 0.061, and 0.955. For the second index group, the MAE, MSE and R\({}^{2}\) of the XGBoost prediction model are 0.031, 0.031 and 0.981, respectively, while the MAE, MSE and R\({}^{2}\) of the SVM prediction model are 0.094, 0.094 and 0.944. The MAE, MSE and R\({}^{2}\) of the RF prediction model are 0.031, 0.031 and 0.978, respectively, and the MAE, MSE and R\({}^{2}\) of the GBDT prediction model are 0.031, 0.031 and 0.981, respectively. The MAE, MSE, and R\({}^{2}\) of the stacking prediction model are 0.031, 0.031, and 0.981, respectively. For the third group of indicators, the XGBoost predictive model exhibits an MAE, MSE, and R\({}^{2}\) of 0.091, 0.091, and 0.933, respectively. The SVM predictive model shows an MAE, MSE, and R\({}^{2}\) of 0.242, 0.303, and 0.776, respectively. For the RF predictive model, the MAE, MSE, and R\({}^{2}\) are 0.091, 0.091, and 0.933, respectively. The GBDT predictive model demonstrates an MAE, MSE, and R\({}^{2}\) of 0.091, 0.091, and 0.933, respectively. Lastly, the stacking predictive model yields an MAE, MSE, and R\({}^{2}\) of 0.091, 0.091, and 0.932, respectively. For the fourth group of indicators, the XGBoost predictive model achieves an MAE, MSE, and R\({}^{2}\) of 0.152, 0.152, and 0.9, respectively. The SVM predictive model presents an MAE, MSE, and R\({}^{2}\) of 0.424, 1.152, and 0.244, respectively. For the RF predictive model, the MAE, MSE, and R\({}^{2}\) are 0.091, 0.091, and 0.94, respectively. The GBDT predictive model demonstrates an MAE, MSE, and R\({}^{2}\) of 0.061, 0.061, and 0.96, respectively. Lastly, the stacking predictive model yields an MAE, MSE, and R\({}^{2}\) of 0.121, 0.121, and 0.92, respectively. For the fifth group of indicators, the XGBoost predictive model exhibits an MAE, MSE, and R\({}^{2}\) of 0.121, 0.121, and 0.92, respectively. The SVM predictive model shows an MAE, MSE, and R\({}^{2}\) of 0.333, 0.333, and 0.781, respectively.