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Characteristics analysis and situation prediction of production safety accidents in non-coal mining

Author

Listed:
  • Wu, Menglong
  • Ye, Yicheng
  • Ke, Lihua
  • Hu, Nanyan
  • Wang, Qihu
  • Li, Yufei

Abstract

Safety accident prediction is the prerequisite for accident prevention and the basis for safety management decision-making. To realize the accurate prediction of non-coal mine safety production situation, the evolution characteristics of non-coal mine safety production situation are analyzed from two perspectives of system characteristics and temporal characteristics, and the complexity of non-coal mine safety production system is elaborated from several perspectives, and its predictability is revealed. For the non-stationary non-coal mine safety production situation, CEEMDAN is used to progressively refine the non-coal mine safety production situation into several high-frequency trend components and low-frequency disturbance components by telescoping translation operations. Aiming at the correlation between the multi-modal components, the multi-modal components are reconstructed according to the distribution characteristics of the fuzzy entropy of the multi-modal components, and the detail, random, trend and residual components are obtained to characterize the composition of the non-coal mine safety production situation. Finally, by assuming the existence of unobservable hidden state sequences and their corresponding observable sequences, the relationship between the hidden states, and the observed states individually and between them is studied, and the HMM prediction model is constructed to achieve accurate prediction of the reconstructed multi-modal components. The results show that the accuracy evaluation indexes such as MAE, MSE and RMSE of the multimodal HMM prediction model for non-coal mine safety production situation prediction is better than those of the comparison model, and the prediction effect under different prediction steps is better than that of the comparison model, and the prediction accuracy is higher. The research is helpful for safety decision-makers to accurately grasp the changes in mine safety production and make correct safety decisions, which can provide theoretical guidance for the safety planning of non-coal mines.

Suggested Citation

  • Wu, Menglong & Ye, Yicheng & Ke, Lihua & Hu, Nanyan & Wang, Qihu & Li, Yufei, 2023. "Characteristics analysis and situation prediction of production safety accidents in non-coal mining," Resources Policy, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:jrpoli:v:83:y:2023:i:c:s0301420723004567
    DOI: 10.1016/j.resourpol.2023.103745
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    References listed on IDEAS

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    1. Menglong Wu & Yicheng Ye & Nanyan Hu & Qihu Wang & Huimin Jiang & Wen Li, 2020. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction," Complexity, Hindawi, vol. 2020, pages 1-14, June.
    2. Shehzad, Khurram & Bilgili, Faik & Zaman, Umer & Kocak, Emrah & Kuskaya, Sevda, 2021. "Is gold favourable than bitcoin during the COVID-19 outbreak? Comparative analysis through wavelet approach," Resources Policy, Elsevier, vol. 73(C).
    3. Weng, Futian & Zhang, Hongwei & Yang, Cai, 2021. "Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 73(C).
    4. Zhang, Mengqi & Jiang, Xin & Fang, Zehua & Zeng, Yue & Xu, Ke, 2019. "High-order Hidden Markov Model for trend prediction in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 1-12.
    5. Xiao Wang & Fan-bao Meng, 2018. "Statistical analysis of large accidents in China’s coal mines in 2016," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 311-325, May.
    6. Cheng Lu & Shuang Li & Kun Xu & Jiao Liu, 2022. "Coal Mine Safety Accidents, Environmental Regulation and Economic Development—An Empirical Study of PVAR Based on Ten Major Coal Provinces in China," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    7. Dai, Xingyu & Li, Matthew C. & Xiao, Ling & Wang, Qunwei, 2022. "COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis," Resources Policy, Elsevier, vol. 79(C).
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    1. Zheng, Xuezhao & Zhou, Bo & Cai, Guobin & Wen, Hu & Liu, Shengkai & Liu, Yin & Zhang, Jianhua & Song, Jian, 2025. "Preparation and properties of nano-composite gel foam for preventing spontaneous combustion of residual coal in mined-out areas," Energy, Elsevier, vol. 338(C).

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