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Machine learning in coal and gas outburst prediction

Peng Ji, Shiliang Shi

Abstract

Artificial intelligence is flourishing, and its research achievements are being extensively applied across various industries. In the field of predicting coal and gas outbursts, methods such as machine learning and deep learning have been widely explored, resulting in accurate prediction accuracy and excellent predictive effects. This has significantly improved the safety of coal mine underground operations.


Keywords

machine learning; coal and gas outburst prediction; deep learning; artificial intelligence; application

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References

1. Wu F, Huo Y, Gao J. Coal mine gas emission prediction method based on forest regression. Industry and Mine Automation 2021; 47(8): 102–106. doi: 10.13272/j.issn.1671-251x.2021010024.

2. Zhang X, Liu F, Li X. Coal mine gas concentration prediction based on wavelet denoising and recurrent neural network. Coal Technology 2020; 39(9): 145–148. doi: 10.13301/j.cnki.ct.2020.09.041.

3. Zheng X, Lai W, Zhang L, Xue S. Quantitative evaluation of the indexes contribution to coal and gas outburst prediction based on machine learning. Fuel 2023; 338: 127389. doi: 10.1016/j.fuel.2023.127389.

4. Ji P, Shi S. Hazard prediction of coal and gas outburst based on the hamming distance artificial intelligence algorithm (HDAIA). Journal of Safety Science and Resilience 2023; 4: 151–158. doi: 10.1016/j.jnlssr.2022.12.001.

5. Xue F, Li X, Xu E. Application of GA-SVM coupling model in prediction coal and gas outburst. Mineral Engineering Research 2022; 37(3): 40–44. doi: 10.13582/j.cnki.1674-5876.2022.03.007.

6. Ji P, Shi S, Lu Y, Li H. Research on risk identification of coal and gas outburst based on PSO-CSA. Mathematical Problem in Engineering 2023; 2023: e5299986. doi: 10.1155/2023/5299986.

7. Lin H, Zhou J, Jin H, et al. Cooperative prediction method of coal and gas outburst risk grade based on feature selection and machine learning algorithm. Journal of Mining & Safety Engineering 2023; 40(2): 361–370. doi: 10.13545/j.cnki.jmse.2022.0010.

8. Fu H, Zhao J, Liu H, et al. Signal identification of fracture in gas bearing coal based on dual strategy coupling optimization. China Safety Science Journal 2022; 32(10): 40–47. doi: 10.16265/j.cnki.issn1003-3033.2022.10.1868.

9. Li B. Research on early warning method of coal and gas outburst based on deep learning and multi-source information fusion [PhD thesis]. Beijing: China University of Mining and Technology; 2021.


DOI: https://doi.org/10.59400/jam.v1i1.74
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