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Variance Analysis in China’s Coal Mine Accident Studies Based on Data Mining

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  • Tianmo Zhou

    (Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Information Institute of the Ministry of Emergency Management of PRC (IIEM), China Coal Information Institute (CCII), Beijing 100029, China)

  • Yunqiang Zhu

    (Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Kai Sun

    (Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

  • Jialin Chen

    (Information Institute of the Ministry of Emergency Management of PRC (IIEM), China Coal Information Institute (CCII), Beijing 100029, China)

  • Shu Wang

    (Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

  • Huazhong Zhu

    (Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

  • Xiaoshuang Wang

    (Beijing Municipal Ecology Environment Bureau Integrating Business Center, Beijing 100048, China)

Abstract

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by “statistical description, risk evaluation, mechanism research, and intelligent reasoning”. (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on “public opinion”, with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China’s safety strategy.

Suggested Citation

  • Tianmo Zhou & Yunqiang Zhu & Kai Sun & Jialin Chen & Shu Wang & Huazhong Zhu & Xiaoshuang Wang, 2022. "Variance Analysis in China’s Coal Mine Accident Studies Based on Data Mining," IJERPH, MDPI, vol. 19(24), pages 1-27, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16582-:d:999258
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    References listed on IDEAS

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