Prediction and Assessment of the Occupational Risks in the Mining Industry Using the Bayes's Theorem


The analysis of existing methods for assessing occupational risks is carried out, and the need for searchinga fundamentally new approach to the assessment and prediction of risks in the mining industry is substantiated. Based on the results of the analysis of modern methods and technologies, it is established that the development of the methodology for assessment and prediction of the occupational risks using Bayes's theorem has significant advantages: simplicity and accessibility for the occupational safety specialists, reproducibility considering many factors of working conditions, as well as the possibility of preventive measures prediction and development.
The application of Bayes's theorem is promising in determining cause-and-effect relationships and predicting the occupational morbidity of the employees, which is also an advantage of this methodology for managing occupational risks in the mining industry. Bayes's approaches to modeling are characterized by high performance, intuitively clear in the form of a graph.
The example is given concerning the application of Bayes's theorem to assess the risk of a fatal incident taking into account the statistics on the mining industry. Also, the simplest types of Bayes’s trust networks were developed reflecting the possibility of establishing cause-and-effect relationships (both for assessment and prediction), and are the basis for further modeling.

1. Onder S. Evaluation of occupational injuries with lost days among opencast coal mine workers through logistic regression models. Safety Science. 2013. Vol. 59. pp. 86–92.
2. Fomin A.I., Malysheva M.N., Pavlov A.F., Popov V.B. Causes and Effect Links of Professional Risks at the Enterprises of Coal Industry of Kuzbass. Bezopasnost truda v promyshlennosti = Occupational Safety in Industry. 2017. № 1. pp. 74–76. (In Russ.).
3. Fomin A.I. Occupational safety management at the mining enterprises: textbook. Kemerovo: KuzGTU, 2018. 262 p. (In Russ.).
4. Statistical analysis of socio-economic costs of accidents at work in the European Union. Eurostat. Available at: (accessed: April 19, 2020). 
5. Health and safety at work in Europe (1999–2007). A statistical portrait. Eurostat. Available at: 006 (accessed: April 19, 2020). 
6. Annual report on the results of the activities of the Siberian Department of the Federal Service on the Environmental, Indsutrial and Nuclear Supervision for 2019. Kemerovo, 2020. 214 p. (In Russ.).
7. Fomin A.I., Khalyavina M.N., Osipova A.A. Occupational injuries and occupational morbidity problems research. Vestnik nauchnogo centra VostNII po promyshlennoj i jekologicheskoj bezopasnosti = Bulletin of the Scientific Center of VostNII on Industrial and Environmental Safety. 2017. № 4. pp. 82–90. (In Russ.).
8.  Joy J. Occupational safety risk management in Australian mining. Occupational Medicine. 2004. Vol. 54. Iss. 5. pp. 311–315.
9. Mining industry, 2019. Resources for future: review. Available at: (accessed: April 19, 2020). (In Russ.).
10. Levashov S.P. Monitoring and analysis of the occupational risks in Russia and abroad: monograph. Kurgan: Kurganskij gosudarstvennyy universitet, 2013. 345 p. (In Russ.).
11. Timofeeva S.S., Murzin M.A. Professional risks of mining industry in the Baikal region. Bezopasnost v tekhnosfere = Safety in technosphere. 2014. Vol. 3. № 3. pp. 37–42. (In Russ.). DOI: 10.12737/4940
12. Tulupev A.L., Nikolenko S.I., Sirotkin A.V. Bayes’s networks: the logical-probabilistic approach. Saint-Petersburg: Nauка, 2006. 608 p. (In Russ.).
13. Litvinov A.R., Kolikov K.S., Ishhneli O.G. Accident and traumatism at coal industry enterprises in 2010–2015. Vestnik nauchnogo centra po bezopasnosti rabot v ugolnoj promyshlennosti = Bulletin of research center for safety in coal industry. 2017. № 2. pp. 6–17. (In Russ.).
DOI: 10.24000/0409-2961-2021-1-79-87
Year: 2021
Issue num: January
Keywords : occupational risk hazard special assessment of working conditions industrial incident Bayes’s theorem Bayes’s networks of trust mining industry prediction and assessment
  • Utyuganova V.V.
    Candidate, Senior Lecturer, FGBOU VO «OmGTU», Omsk, Russia
  • Serdyuk V.S.
    Dr. Sci. (Eng.), Prof. FGBOU VO «OmGTU», Omsk, Russia
  • Fomin A.I.
    Dr. Sci. (Eng.), Leading Researcher (AO «NTs VostNII», Kemerovo, Russia), Head of the Department (KuzSTU, Kemerovo, Russia)