TY - JOUR
T1 - Signatures of criticality in mining accidents and recurrent neural network forecasting model
AU - Doss, Karan
AU - Hanshew, Alissa S.
AU - Mauro, John C.
N1 - Funding Information:
We would like to acknowledge Dr. Clive Randall, Dr. Susan B. Sinnott, the Department of Materials Science and Engineering, and the Materials Research Institute at Penn State for supporting this work.
Funding Information:
We would like to acknowledge Dr. Clive Randall, Dr. Susan B. Sinnott, the Department of Materials Science and Engineering, and the Materials Research Institute at Penn State for supporting this work.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.physa.2019.122656
DO - 10.1016/j.physa.2019.122656
M3 - Article
AN - SCOPUS:85072288183
VL - 537
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
SN - 0378-4371
M1 - 122656
ER -