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Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

Author

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  • Anurag Yedla

    (Department of Computer Science, Iowa State University, Ames, IA 50014, USA)

  • Fatemeh Davoudi Kakhki

    (Department of Technology, College of Engineering, San Jose State University, San Jose, CA 95192, USA)

  • Ali Jannesari

    (Department of Computer Science, Iowa State University, Ames, IA 50014, USA)

Abstract

Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work.

Suggested Citation

  • Anurag Yedla & Fatemeh Davoudi Kakhki & Ali Jannesari, 2020. "Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations," IJERPH, MDPI, vol. 17(19), pages 1-17, September.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7054-:d:420263
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    References listed on IDEAS

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    1. Lluís Sanmiquel & Marc Bascompta & Josep M. Rossell & Hernán Francisco Anticoi & Eduard Guash, 2018. "Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques," IJERPH, MDPI, vol. 15(3), pages 1-11, March.
    2. Shields, Michael D. & Teferra, Kirubel & Hapij, Adam & Daddazio, Raymond P., 2015. "Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 310-325.
    3. Zeynep H. Kilimci & Selim Akyokus, 2018. "Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification," Complexity, Hindawi, vol. 2018, pages 1-10, October.
    4. Rivas, T. & Paz, M. & Martín, J.E. & Matías, J.M. & García, J.F. & Taboada, J., 2011. "Explaining and predicting workplace accidents using data-mining techniques," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 739-747.
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    Cited by:

    1. Mauricio A. Ramírez-Moreno & Patricio Carrillo-Tijerina & Milton Osiel Candela-Leal & Myriam Alanis-Espinosa & Juan Carlos Tudón-Martínez & Armando Roman-Flores & Ricardo A. Ramírez-Mendoza & Jorge de, 2021. "Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    2. Mohamed Zul Fadhli Khairuddin & Puat Lu Hui & Khairunnisa Hasikin & Nasrul Anuar Abd Razak & Khin Wee Lai & Ahmad Shakir Mohd Saudi & Siti Salwa Ibrahim, 2022. "Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    3. Ana Maria Mihaela Iordache & Codruța Cornelia Dura & Cristina Coculescu & Claudia Isac & Ana Preda, 2021. "Using Neural Networks in Order to Analyze Telework Adaptability across the European Union Countries: A Case Study of the Most Relevant Scenarios to Occur in Romania," IJERPH, MDPI, vol. 18(20), pages 1-28, October.
    4. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    5. Maryam Pishgar & Salah Fuad Issa & Margaret Sietsema & Preethi Pratap & Houshang Darabi, 2021. "REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health," IJERPH, MDPI, vol. 18(13), pages 1-42, June.
    6. Antonella Pireddu & Angelico Bedini & Mara Lombardi & Angelo L. C. Ciribini & Davide Berardi, 2024. "A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management," IJERPH, MDPI, vol. 21(7), pages 1-26, June.

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