Prediction of coal-gas compound dynamic disaster based on convolutional neural network

Wang Kai; Li Kangnan; Du Feng; Zhang Xiang; Wang Yanhai; Zhou Jiaxu

doi:10.19606/j.cnki.jmst.2023.05.003

Volume 8 Issue 5

Oct. 2023

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Article Navigation > Journal of Mining Science and Technology > 2023 > 8(5): 613-622

Wang Kai, Li Kangnan, Du Feng, Zhang Xiang, Wang Yanhai, Zhou Jiaxu. Prediction of coal-gas compound dynamic disaster based on convolutional neural network[J]. Journal of Mining Science and Technology, 2023, 8(5): 613-622. doi: 10.19606/j.cnki.jmst.2023.05.003

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Prediction of coal-gas compound dynamic disaster based on convolutional neural network

doi: 10.19606/j.cnki.jmst.2023.05.003

Wang Kai^{1, 2
,},
Li Kangnan^{1, 2},
Du Feng^{1, 2},
Zhang Xiang^{1, 2},
Wang Yanhai^{1, 2},
Zhou Jiaxu²

1.
Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Received Date: 2023-02-17
Rev Recd Date: 2023-04-03
Publish Date: 2023-10-31

Abstract

Abstract

As deep mining becomes prevalent in China's coal mining industry, coal-gas compound dynamic disasters pose increasing threat to the safety production of coal mines. This paper adopts the field data of Pingmei No. 8 coal mine for analysis, with the attempt to predict coal-gas compound dynamic disaster through convolutional neural network. Following the routine of the big data processing, we first employed Box-plot analysis and multiple interpolation method(MI)to clean the data. Combined with grey relation analysis(GRA), we established a coal-gas compound dynamic disaster index system. Then, principal component analysis(PCA)is used for dimensionality reduction of the data. Combined with the convolution neural network(CNN)in deep learning, we established the coal-gas compound dynamic disaster prediction model based on BMGP-CNN. The field data is used to compare and verify this model with BP, random forest(RF), support vector machine(SVM)and artificial neural network(ANN). It is found that BMGP-CNN model yields prediction results with satisfactory accuracy and quick convergence. The results offer implications for the prediction and prevention of coal-gas compound dynamic disasters.
- coal-gas compound dynamic disaster,
- deep learning,
- big data,
- index system,
- prediction model

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References

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Prediction of coal-gas compound dynamic disaster based on convolutional neural network

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