## 4 Conclusions  
In this study, a machine learning hybrid model GA-RF was developed for prediction of oxygen concentration of the working surface return airflow corner during coal mine. An amount of 22104 working faces of Shangwan Coal Mine in Shendong mining area were used as the research object. Through the analysis of coal seam gas components and the oxidation experiment of residual coal, it was determined that the low oxygen gas in the goal mainly includes the nitrogen produced by the desorption of broken coal and the low oxygen gas produced after the consumption of oxygen by the oxidation of residual coal, making the goal become a high nitrogen and low oxygen environment. Influenced by air leakage channel, surface climate change, working face mining speed, and other factors, the pressure difference between the goaf and the working face changes dynamically. Under the effect of pressure difference and negative pressure ventilation, low oxygen gas pushes out from the return corner, resulting in low oxygen.  
Field data were collected through up-down linkage of mine. Training samples were constructed, and input and output indexes of prediction model were determined. Using GA to optimize the RF model parameters, including the number of trees and the number of leaves per tree, and MAE, MAPE, and RMSE were used to evaluate the prediction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE (\%)** & **MAE** \\ \hline GA-RF & 0.2713 & 1.09 & 0.1961 \\ RF (leaf = 1) & 0.3628 & 1.57 & 0.2813 \\ RF (leaf = 2) & 0.3797 & 1.62 & 0.2895 \\ RF (leaf = 3) & 0.3787 & 1.61 & 0.2883 \\ RF (leaf = 4) & 0.3814 & 1.62 & 0.2901 \\ RF (leaf = 5) & 0.3846 & 1.64 & 0.2942 \\ GA-BPNN & 0.3941 & 1.65 & 0.2950 \\ BPNN & 0.4354 & 1.94 & 0.3455 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical Value of Performance Evaluation Index of GA-RF, RF, GA-BPNN and BPNN.  
Figure 17: GA-RF, RF, GA-BPNN, BPNN absolute error percentage box.  
performance of the model. Compared with RF, GA-BPNN, and BPNN models, the number of hidden neurons was firstly optimized when GA optimized BPNN, and the network topology was determined. On this basis, the initial weights and thresholds of the network were optimized. The results show that GA-RF has high generalization ability and prediction accuracy, and GA has certain optimization effect on prediction accuracy. The RMSE, MAPE, and MAE values of the GA-RF model are about 0.2713, 1.09, 0.1961, respectively. Therefore, it is suggested to use GA-RF model to predict the oxygen concentration in the corner of return air. Based on the change of surface climate conditions and the data of the roof of the working face, the oxygen concentration at the return air corner is predicted without affecting the ventilation system, and the prevention and control measures for low oxygen are prepared in advance to ensure the safe production of workers. At the same time, according to the actual situation of the mine, GA-RF model can also be used to predict similar abnormal gas emission problems in goaf, such as CH\({}_{4}\) overrun and CO overrun in return air corner.  
Conceptualization, K.W. and Z.A.; methodology, Z.A. and W.Z.; validation, Z.A., W.Z. and Q.F.; formal analysis, Z.A. and A.Z.; investigation, Z.A. and Q.F.; resources, Z.A., W.Z. and A.Z.; writing--original draft preparation, Z.A.; writing--review and editing, K.W., Z.A. and W.Z.; supervision, K.W.; funding acquisition, K.W. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Beijing Natural Science Foundation (Grant number 822070), the Innovative Research Group Project of the National Natural Science Foundation of China (Grant number 52121003) and the National Natural Science Foundation of China (Grant numbers 52130409 and 51874314).  
Not applicable.  
The authors confirm that the data supporting the findings of this study are available within the article.  
The authors declare no conflict of interest.