Influencing Factors Analysis and Prediction of Gas Emission in Mining Face

Ruoyu Bao, Quanchao Feng () and Changkui Lei
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Ruoyu Bao: China Academy of Safety Science and Technology, Beijing 100012, China
Quanchao Feng: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Changkui Lei: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Sustainability, 2025, vol. 17, issue 2, 1-15

Abstract: Mine gas emission is one of the main causes of gas disasters. In order to achieve the accurate prediction of gas emission, a gas emission prediction model based on the random forest (RF) method was proposed in combination with the analysis of its influencing factors. The prediction results were compared with the support vector regression (SVR) and BP neural network (BPNN) methods, and then they were verified and analyzed through the Dongqu coal mine. The results show that the gas emission prediction model based on random forest has strong generalization and robustness, and RF has a wide range of parameter adaptation during the modeling process. When the number of trees (n tree ) exceeds 100, its training error tends to stabilize, and changes in n tree have no substantial impact on the prediction performance. The SVR prediction model has significant bias in both the training and testing stages. Meanwhile, the BPNN model has excellent prediction results in the training phase, but there is a large error in the testing stage, which indicates that there is an “overfitting” phenomenon in the training stage, resulting in weak generalization. The evaluation of variable importance shows that the extraction rate, coal seam depth, daily production, gas content in adjacent layers, and coal seam thickness have a significant impact on gas emission. Meanwhile, through application analysis, it is further demonstrated that the random forest method has high accuracy, strong stability, and universality, and it can achieve good predictive performance without the need for complex parameter settings and optimization, making it is very suitable for predicting gas emission.

Keywords: gas emission; random forest; support vector regression machine; BP neural networks; variable importance evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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