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

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

Physica A: Statistical Mechanics and its Applications, 2020, vol. 537, issue 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.

Keywords: Mining safety; Self-organized criticality; Time-series forecasting; Machine learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:537:y:2020:i:c:s037843711931516x

DOI: 10.1016/j.physa.2019.122656

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