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.
| Original language | English (US) |
|---|---|
| Article number | 122656 |
| Journal | Physica A: Statistical Mechanics and its Applications |
| Volume | 537 |
| DOIs | |
| State | Published - Jan 1 2020 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Condensed Matter Physics
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Signatures of criticality in mining accidents and recurrent neural network forecasting model. / Doss, Karan; Hanshew, Alissa S.; Mauro, John.
In: Physica A: Statistical Mechanics and its Applications, Vol. 537, 122656, 01.01.2020.Research output: Contribution to journal › Article
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
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.
UR - http://www.scopus.com/inward/record.url?scp=85072288183&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072288183&partnerID=8YFLogxK
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 -