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Signatures of criticality in mining accidents and recurrent neural network forecasting model

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  • Doss, Karan
  • Hanshew, Alissa S.
  • Mauro, John C.

Abstract

We report signatures of criticality in mining accident data obtained from the Mine Accident, Injury and Illness Report form (MSHA Form 7000-1). This work builds on the hypothesis that workplace accident statistics follow self-organized criticality (Mauro et al., 2018). “1/f noise,” a distinct feature of critical systems, is extracted from this database and is used to forecast accident trends using a long short-term memory (LSTM) recurrent neural network (RNN). The algorithm used for extracting this noise is applicable to data available in any standard worker’s compensation database. We also report a Pareto distribution in the number of accidents in relation to employee mine experience, implying a strong correlation between experience and susceptibility to accidents.

Suggested Citation

  • Doss, Karan & Hanshew, Alissa S. & Mauro, John C., 2020. "Signatures of criticality in mining accidents and recurrent neural network forecasting model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s037843711931516x
    DOI: 10.1016/j.physa.2019.122656
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    References listed on IDEAS

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    1. Mauro, John C. & Diehl, Brett & Marcellin, Richard F. & Vaughn, Daniel J., 2018. "Workplace accidents and self-organized criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 284-289.
    2. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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