Development of a mathematical forecasing model for occupational injuries in mining

The mining industry is characterized by the influence of special hazardous and harmful factors that, in turn, is associated with the risk of occupational injuries and occupational diseases. In order to reduce the risks, modern methods of accounting, analysis and forecasting of injury rates are required. Statistical methods of data processing are consistent with these requirements. The basis for the study was evidence from an appraisal audit of five domestic enterprises of the mining complex. In the course of processing injury statistics for the period from 1975 to 2017 with the regression method, a mathematical model was obtained. The model makes it possible to predict the number of accidents at work for the next year. To some extent it confirmed the hypothesis which was put forward at the beginning of the study. As practice has shown, the mining enterprises of the Russian Federation keep statistics of injuries in the form of aggregated data, which makes it impossible to predict the probable causes of accidents. The study proposed a form for the correct collection of statistics of injuries at mining enterprises. The form helps to obtain an improved model for predicting not only the number of accidents for the next year, but also the probable causes of the accidents, which, in turn, can serve as a basis for determining labor safety arrangements for the next year.

Keywords: Shewhart control chart, SPSS Statistics, mathematical forecasting model, linear regression, mining, occupational injuries, risk management, health and safety management system, audit.
For citation:

Shalimova A.V., Filin A.E. Development of a mathematical forecasing model for occupational injuries in mining. MIAB. Mining Inf. Anal. Bull. 2021;(2—1):209-219. [In Russ]. DOI: 10.25018/0236-1493-2021-21-0-209-219.

Issue number: 2
Year: 2021
Page number: 209-219
ISBN: 0236-1493
UDK: 331.46
DOI: 10.25018/0236-1493-2021-21-0-209-219
Article receipt date: 04.12.2020
Date of review receipt: 15.12.2020
Date of the editorial board′s decision on the article′s publishing: 01.02.2021
About authors:

Shalimova A.V.1, assistant of Technosphere safety Department of NUST «MISiS», e-mail:;
Filin A.E.1, Dr. Sci. (Eng.), professor of Technosphere safety Department of NUST «MISiS». E-mail:;
1 NUST «MISiS», Moscow, Russia.


For contacts:

1. Pototskii E.P., Firsova V.M., Sakharova E.A. Account of joint effect of the complex of harmful factors and analysis of the influence of production factor of chemical nature on the level of professional risk. Izvestiya Ferrous Metallurgy, 2018, 61(1), pp. 35—39. [In Russ]. DOI: 10.17073/0368—0797—2018—1-35—39.

2. Mikhaylova V.N., Balovtsev S.V., Khristoforov N.R. Assessment of occupational hearing disorder on the violation of article 27 of federal law 52 in mining. MIAB. Mining Inf. Anal. Bull. 2018, no. 5, pp. 228—234. DOI: 10.25018/0236-1493-2018-5-0-228-234. [In Russ].

3. Filin A.E., Zinovieva O.M., Kolesnikova L.A., Merkulova A.M. Prospects of safety control in combination of mining and metallurgy industries. Eurasian Mining. 2018. no. 1. Рp. 31—34. DOI: 10.17580/em.2018.01.07.

4. Vinogradova O.V. Human errors as a factor of production risk in the mining industry. MIAB. Mining Inf. Anal. Bull. 2020;(6—1):137—145. [In Russ]. DOI: 10.25018/0236-14932020-61-0-137-145.

5. Box G., Narasimhan S. (2010) Rethinking Statistics for Quality Control. — Quality Engineering, vol. 22, pp.60—72.

6. Roughton, J., Crutchfield, N., & Waite, M. (2019). Assessing Your Safety Management System. Safety Culture, 345—374. doi:10.1016/b978-0-12-814663-7.00014-5.

7. Prinyatiye resheniy. Metod analiza iyerarkhiy [Making decisions. Method of analysis of hierarchies] per. s angl. R.G. Vachnadze. Moscow: Radio i svyaz’. 1993, 278 p. [In Russ].

8. Laal F., Pouyakian M., Madvari R.F., Khoshakhlagh A.H., Halvani G.H., Investigating the Impact of Establishing Integrated Management Systems on Accidents and Safety Performance Indices: A case study, Safety and Health at Work (2018), doi: 10.1016/j. shaw.2018.04.001.

9. Tekhnologiya analiza dannyh. Metody otbora peremennyh v regressionnye modeli. [Elektronnyj resurs]. URL: (data obrashcheniya: 15.10.2020). [In Russ].

10. Balovtsev S.V., Skopintseva O.V., Kolikov K.S. Aerological risk management in designing, operation, closure and temporary shutdown of coal mines. MIAB. Mining Inf. Anal. Bull. 2020;(6):85—94. [In Russ]. DOI: 10.25018/0236—1493—2020—6-0—85—94.

11. Skopintseva O.V., Ganova S.D., Buzin A.A., Fedotova V.P. Measures to reduce dusting during loading and transportation of solid mineral resources. Gornyi Zhurnal. 2019, no. 12, pp. 76—79. DOI: 10.17580/gzh.2019.12.16. [In Russ].

12. Ganova S.D., Skopintseva O.V., Isaev O.N. On the issue of studying the composition of hydrocarbon gases of coals and dust to predict their potential hazard. Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering. 2019, t. 330, no. 6, pp. 109—115. [In Russ].

13. Q. Liu, X. Meng, X. Li, X. Luo Q. Liu, X. Meng, X. Li, X. Luo. Risk precontrol continuum and risk gradient control in underground coal mining. Process Safety and Environmental Protection. 2019. Vol. 129. Pp. 210—219. DOI: 10.1016/j.psep.2019.06.03.

14. Kulikova E. Yu. Defects of urban underground structure and their prediction. IOP Conference Series: Materials Science and Engineering, 2018, 451(1), 012108. DOI: 10.1088/1757—899X/451/1/012108.

15. Kulikova E. Yu., Balovtsev S.V. Risk control system for the construction of urban underground structures. IOP Conference Series: Materials Science and Engineering, 2020, 962 042020.—899X/962/4/042020.

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