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Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

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  • Kai Yu

    (College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    Min An Institute of Emergency and Safety Management of Qingdao West Coast New Area, Qingdao 266590, China)

  • Lujie Zhou

    (College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Pingping Liu

    (College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jing Chen

    (College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Dejun Miao

    (College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jiansheng Wang

    (Huaneng Lingtai Shaozhai Coal Industry Co., Ltd., Pingliang 744400, China)

Abstract

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises.

Suggested Citation

  • Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4028-:d:957886
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    References listed on IDEAS

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    Cited by:

    1. You, Qi & Yu, Kai & Zhou, Lujie & Zhang, Jing & Lv, Maoyun & Wang, Jiansheng, 2023. "Research on risk analysis and prevention policy of coal mine workers' group behavior based on evolutionary game," Resources Policy, Elsevier, vol. 80(C).

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