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Research on assisting coal mine hazard investigation for accident prevention through text mining and deep learning

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  • Tingjiang, Tan
  • Enyuan, Wang
  • Ke, Zhao
  • Changfang, Guo

Abstract

Hazard investigation, an effective means of preventing accidents in coal mines, is in a dilemma of “lack of understanding and management”, because the hazard data recorded and saved in text cannot be effectively used. Against this background, this research combines deep learning and text mining technologies to efficiently analyze hazard records for assisting hazard investigation: (1) 3900 historical hazard data from the Fenghuangtai coal mine are used for statistical analysis and text preprocessing; (2) Latent Dirichlet Allocation (LDA) topic analysis is utilized to obtain dangerous topics and feature words; (3) The convolutional neural network (CNN) algorithm is used to classify the danger automatically; (4) The rules between hazard categories and units are visually displayed through images. The findings demonstrate that this paper can effectively classify new text data and identify latent hazard themes that represent reality from coal mine data without relying on expert judgment. The visualization method is appropriate for translating the mining results into specific visual information, which is practical for worker safety education and training and managing hazard investigation. This study provides an innovative perspective on efficiently discovering and utilizing coal mine concealed danger information for accident prevention.

Suggested Citation

  • Tingjiang, Tan & Enyuan, Wang & Ke, Zhao & Changfang, Guo, 2023. "Research on assisting coal mine hazard investigation for accident prevention through text mining and deep learning," Resources Policy, Elsevier, vol. 85(PB).
    • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pb:s0301420723005135
    DOI: 10.1016/j.resourpol.2023.103802
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    References listed on IDEAS

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    1. Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    2. Yan Feng & Hong Chen & Yingyu Zhang & Linlin Jing, 2019. "The hybrid systems method integrating human factors analysis and classification system and grey relational analysis for the analysis of major coal mining accidents," Systems Research and Behavioral Science, Wiley Blackwell, vol. 36(4), pages 564-579, July.
    3. Xu, Jiuping & Gao, Wen & Xie, Heping & Dai, Jingqi & Lv, Chengwei & Li, Meihui, 2018. "Integrated tech-paradigm based innovative approach towards ecological coal mining," Energy, Elsevier, vol. 151(C), pages 297-308.
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    Cited by:

    1. Binfeng Shi, 2024. "Transmission mechanism of public concern in waste-sorting policy: Evidence from text mining," Energy & Environment, , vol. 35(3), pages 1616-1636, May.

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