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Network analysis of coal mine hazards based on text mining and link prediction

Author

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  • Ze Wang

    (School of Economics and Management, China University of Geosciences, Beijing 100083, P. R. China2Key Laboratory of Carrying Capacity, Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, P. R. China3Key Laboratory of Strategic Studies, Ministry of Natural Resources, Beijing 100082, P. R. China)

  • Huajiao Li

    (School of Economics and Management, China University of Geosciences, Beijing 100083, P. R. China2Key Laboratory of Carrying Capacity, Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, P. R. China3Key Laboratory of Strategic Studies, Ministry of Natural Resources, Beijing 100082, P. R. China)

  • Renwu Tang

    (School of Government, Beijing Normal University, Beijing 100083, P. R. China)

Abstract

Hazards are a potential source of harm and damage hiding in shadow zones. Without control, they may accumulate and interact with other types of hazards. In the harsh and complicated circumstance, especially, the deep underground space of coal mines, complex and nonlinear interactions among hazards multiply the probabilities that a hazard turns into accidents, more seriously, its effect may trigger more correlated hazards to worsen the accidents and bring huge loss of lives and assets. Therefore, identifying the correlations among hazards and understanding the complexity of interactions among coal mine hazards are significant for ensuring the safety of coal production. From this standpoint, we propose a hybrid method combing text mining and complex network method. First, we abstract the dangerous hazards from a large amount of text data. Then, we establish the coal mine hazard network (CMHN) to capture correlations among hazards. Finally, we analyze and predict the correlations among hazards based on CMHN. Through which, we find the fault-prone hazard and the recurrent hazards, more importantly, we figure out the nonlinear correlations among hazards and reveal the connection preference of hazards. Furthermore, we forecast the unknown correlations among hazards to take precautions of them. This study could be helpful for making prevention strategies for safety management in the coal mine and other highly dangerous industries.

Suggested Citation

  • Ze Wang & Huajiao Li & Renwu Tang, 2019. "Network analysis of coal mine hazards based on text mining and link prediction," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(07), pages 1-22, July.
    • Handle: RePEc:wsi:ijmpcx:v:30:y:2019:i:07:n:s0129183119400096
    DOI: 10.1142/S0129183119400096
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    Citations

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    Cited by:

    1. Xiaofang Wo & Guichen Li & Yuantian Sun & Jinghua Li & Sen Yang & Haoran Hao, 2022. "The Changing Tendency and Association Analysis of Intelligent Coal Mines in China: A Policy Text Mining Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    2. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    3. Shen, Junjie & Huang, Shupei, 2022. "Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method," Resources Policy, Elsevier, vol. 75(C).
    4. Jiangshi Zhang & Yongtun Li & Jingru Wu & Xiaofeng Ren & Yaona Wang & Hongfu Jia & Mengyu Xie, 2024. "Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge," Sustainability, MDPI, vol. 16(20), pages 1-16, October.

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