A Comprehensive Assessment of Mining Accident Severity Using Machine Learning Methods
DOI:
https://doi.org/10.18485/khwvt756Keywords:
behavioral factors, mining industry, safety, accidents, machine learningAbstract
The mining sector is recognized as one of the riskiest in the world. However, there is a lack of a comprehensive understanding of the factors influencing accident severity. In this study, eight machine learning (ML) methods were systematically evaluated, among which Gradient Boosting and Random Forest emerged as the top-performing models. These ensemble models demonstrated consistently high performance across multiple evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC AUC), highlighting their robustness and reliability in distinguishing between fatal and non-fatal accident outcomes. Across both impurity-based (Gini importance) and robust model-agnostic (SHAP) frameworks, Risk Taking, Age, and Experience emerge as the most influential predictors. Social engagement metrics vary in importance, collectively suggesting that social support systems may play a meaningful role in moderating the severity of accidents. These findings substantiate the utility of machine learning not only for accurate outcome classification but also for elucidating the multifaceted drivers of accident severity, thereby informing targeted intervention and prevention strategies.
Downloads
References
1. Aliabadi, M. M., Aghaei, H., Kalatpour, O., Soltanian, A. R., & Tabib, M. S. (2018). Effects of human and organizational deficiencies on workers’ safety behavior at a mining site in Iran. Epidemiology and Health, 40, e2018019. https://doi.org/10.4178/epih.e2018019
2. Amponsah-Tawiah, K., & Mensah, J. (2016). The impact of safety climate on safety-related driving behaviors. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 48–55. https://doi.org/10.1016/j.trf.2016.04.002
3. Baraza, X., Cugueró-Escofet, N., & Rodríguez-Elizalde, R. (2023). Statistical analysis of the severity of occupational accidents in the mining sector. Journal of Safety Research, 86, 364–375. https://doi.org/10.1016/j.jsr.2023.07.015
4. Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behavior. Journal of Research in Personality, 25(3), 285–301. https://doi.org/10.1016/0092-6566(91)90021-H
5. Choudhry, R. M., & Fang, D. (2008). Why operatives engage in unsafe work behavior: Investigating factors on construction sites. Safety Science, 46(4), 566–584. https://doi.org/10.1016/j.ssci.2007.06.027
6. Cvetkovic, V. M. (2019). Risk Perception of Building Fires in Belgrade. International Journal of Disaster Risk Management, 81–91. https://doi.org/10.18485/ijdrm.2019.1.1.5
7. DeJoy, D. M., Della, L. J., Vandenberg, R. J., & Wilson, M. G. (2010). Making work safer: Testing a model of social exchange and safety management. Journal of Safety Research, 41(2), 163–171. https://doi.org/10.1016/j.jsr.2010.02.001
8. Duma, K., Husodo, A. H., Soebijanto, & Maurits, L. S. (2014). The policy of control health and safety and the risk factors in the coal mining of East Kalimantan. BMC Public Health, 14(Suppl 1), O26. https://doi.org/10.1186/1471-2458-14-S1-O26
9. Forssten, M. P., Bass, G. A., Ismail, A. M., Mohseni, S., & Cao, Y. (2021). Predicting 1-year mortality after hip fracture surgery: An evaluation of multiple machine learning approaches. Journal of Personalized Medicine, 11(8), 727. https://doi.org/10.3390/jpm11080727
10. Fugas, C. S., Silva, S. A., & Meliá, J. L. (2012). Another look at safety climate and safety behavior: Deepening the cognitive and social mediator mechanisms. Accident Analysis & Prevention, 45, 468–477. https://doi.org/10.1016/j.aap.2011.08.013
11. Glendon, A. I., & Litherland, D. K. (2001). Safety climate factors, group differences and safety behaviour in road construction. Safety Science, 39(3), 157–188. https://doi.org/10.1016/S0925-7535(01)00006-6
12. Guo, X., & Kapucu, N. (2019). Examining Stakeholder Participation in Social Stability Risk Assessment for Mega Projects using Network Analysis. International Journal of Disaster Risk Management, 1–31. https://doi.org/10.18485/ijdrm.2019.1.1.1
13. Han, S., Saba, F., Lee, S., Mohamed, Y., & Peña-Mora, F. (2014). Toward an understanding of the impact of production pressure on safety performance in construction operations. Accident Analysis & Prevention, 68, 106–116. https://doi.org/10.1016/j.aap.2013.10.007
14. Javaid, A., Siddique, M. A., Reshi, A. A., Mui-zzud-din, Rustam, F., Lee, E., & Rupapara, V. (2023). Coal mining accident causes classification using voting-based hybrid classifier (VHC). Journal of Ambient Intelligence and Humanized Computing, 14(10), 13211–13221. https://doi.org/10.1007/s12652-022-03779-z
15. Jiang, C., Zhu, S., Hu, H., An, S., Su, W., Chen, X., Li, C., & Zheng, L. (2022). Deep learning model based on big data for water source discrimination in an underground multiaquifer coal mine. Bulletin of Engineering Geology and the Environment, 81(1), 26. https://doi.org/10.1007/s10064-021-02535-5
16. Jitwasinkul, B., Hadikusumo, B. H. W., & Memon, A. Q. (2016). A Bayesian belief network model of organizational factors for improving safe work behaviors in Thai construction industry. Safety Science, 82, 264–273. https://doi.org/10.1016/j.ssci.2015.09.027
17. Joe-Asare, T., Stemn, E., & Amegbey, N. (2023). Relationships among causal factors influencing mine accidents using structural equation modelling. International Journal of Injury Control and Safety Promotion, 30(4), 643–651. https://doi.org/10.1080/17457300.2023.2248491
18. Kahraman, M. M. (2021). Analysis of mining lost time incident duration influencing factors through machine learning. Mining, Metallurgy & Exploration, 38(2), 1031–1039. https://doi.org/10.1007/s42461-021-00396-w
19. Kapp, E. A. (2012). The influence of supervisor leadership practices and perceived group safety climate on employee safety performance. Safety Science, 50(4), 1119–1124. https://doi.org/10.1016/j.ssci.2011.11.011
20. Kecojevic, V., Komljenovic, D., Groves, W., & Radomsky, M. (2007). An analysis of equipment-related fatal accidents in U.S. mining operations: 1995–2005. Safety Science, 45(8), 864–874. https://doi.org/10.1016/j.ssci.2006.08.024
21. Kong, D., Liu, S., & Xiang, J. (2018). Political promotion and labor investment efficiency. China Economic Review, 50, 273–293. https://doi.org/10.1016/j.chieco.2018.05.002
22. Li, Y., Wu, X., Luo, X., Gao, J., & Yin, W. (2019). Impact of safety attitude on the safety behavior of coal miners in China. Sustainability, 11(22), 6382. https://doi.org/10.3390/su11226382
23. Lyra, M. G. (2019). Challenging extractivism: Activism over the aftermath of the Fundão disaster. The Extractive Industries and Society, 6(3), 897–905. https://doi.org/10.1016/j.exis.2019.05.010
24. Melamed, S., Luz, J., Najenson, T., Jucha, E., & Green, M. (1989). Ergonomic stress levels, personal characteristics, accident occurrence and sickness absence among factory workers. Ergonomics, 32(9), 1101–1110. https://doi.org/10.1080/00140138908966877
25. Mohaghegh, Z., & Mosleh, A. (2009). Measurement techniques for organizational safety causal models: Characterization and suggestions for enhancements. Safety Science, 47(10), 1398–1409. https://doi.org/10.1016/j.ssci.2009.04.002
26. Mullen, J. (2004). Investigating factors that influence individual safety behavior at work. Journal of Safety Research, 35(3), 275–285. https://doi.org/10.1016/j.jsr.2004.03.011
27. Neal, A., Griffin, M. A., & Hart, P. M. (2000). The impact of organizational climate on safety climate and individual behavior. Safety Science, 34(1–3), 99–109. https://doi.org/10.1016/S0925-7535(00)00008-4
28. Okpan, O., Okwose, I., Edwards, H. O.-E., & Sanni, F. (2025). Examining the Challenges in Implementing Occupational Health and Safety in the Selected Construction Companies Across Nigerian Coastal Cities. International Journal of Disaster Risk Management, 313–332. https://doi.org/10.18485/ijdrm.2025.7.2.17
29. Omachi, C. Y., Siani, S. M. O., Chagas, F. M., Mascagni, M. L., Cordeiro, M., Garcia, G. D., Thompson, C. C., Siegle, E., & Thompson, F. L. (2018). Atlantic Forest loss caused by the world’s largest tailing dam collapse (Fundão Dam, Mariana, Brazil). Remote Sensing Applications: Society and Environment, 12, 30–34. https://doi.org/10.1016/j.rsase.2018.08.003
30. Ones, D. S. (2002). Introduction to the special issue on counterproductive behaviors at work. International Journal of Selection and Assessment, 10(1–2), 1–4. https://doi.org/10.1111/1468-2389.00188
31. Pinelli, J. P., Esteva, M., Rathje, E. M., Roueche, D., Brandenberg, S. J., Mosqueda, G., Padgett, J., & Haan, F. (2020). Disaster risk management through the DesignSafe cyberinfrastructure. International Journal of Disaster Risk Science, 11(6), 719–734. https://doi.org/10.1007/s13753-020-00320-8
32. Pons, D. J. (2016). Pike River mine disaster: Systems-engineering and organisational contributions. Safety, 2(4), 21. https://doi.org/10.3390/safety2040021
33. Sandoval, V., Voss, M., Flörchinger, V., Lorenz, S., & Jafari, P. (2023). Integrated disaster risk management (IDRM): Elements to advance its study and assessment. International Journal of Disaster Risk Science, 14(3), 343–356. https://doi.org/10.1007/s13753-023-00490-1
34. Seo, H. C., Lee, Y. S., Kim, J. J., & Jee, N. Y. (2015). Analyzing safety behaviors of temporary construction workers using structural equation modeling. Safety Science, 77, 160–168. https://doi.org/10.1016/j.ssci.2015.03.010
35. Sharma, M., & Maity, T. (2024). Review on machine learning-based underground coal mines gas hazard identification and estimation techniques. Archives of Computational Methods in Engineering, 31(1), 371–388. https://doi.org/10.1007/s11831-023-09982-1
36. Tharaldsen, J. E., Olsen, E., & Rundmo, T. (2008). A longitudinal study of safety climate on the Norwegian continental shelf. Safety Science, 46(3), 427–439. https://doi.org/10.1016/j.ssci.2007.05.006
37. Wen, J., Wan, C., Ye, Q., Yan, J., & Li, W. (2023). Disaster risk reduction, climate change adaptation and their linkages with sustainable development over the past 30 years: A review. International Journal of Disaster Risk Science, 14(1), 1–13. https://doi.org/10.1007/s13753-023-00472-3
38. Xie, X., Shen, S., Fu, G., Shu, X., Hu, J., Jia, Q., & Shi, Z. (2022). Accident case data–accident cause model hybrid-driven coal and gas outburst accident analysis: Evidence from 84 accidents in China during 2008–2018. Process Safety and Environmental Protection, 165, 857–871. https://doi.org/10.1016/j.psep.2022.05.048
39. Yang, X., Krul, K., & Sims, D. (2022). Uncovering coal mining accident coverups: An alternative perspective on China’s new safety narrative. Safety Science, 148, 105637. https://doi.org/10.1016/j.ssci.2021.105637
40. Yaşlı, F., & Bolat, B. (2018). A Bayesian network analysis for occupational accidents of mining sector. In Proceedings of the International Symposium for Production Research (pp. 781–799). Springer. https://doi.org/10.1007/978-3-319-92267-6_63
41. Yedla, A., Davoudi Kakhki, F., & Jannesari, A. (2020). Predictive modeling for occupational safety outcomes and days away from work analysis in mining operations. International Journal of Environmental Research and Public Health, 17(19), 7054. https://doi.org/10.3390/ijerph17197054
42. Zhang, J., Fu, J., Hao, H., Fu, G., Nie, F., & Zhang, W. (2020). Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics. Process Safety and Environmental Protection, 136, 78–91. https://doi.org/10.1016/j.psep.2020.01.024
43. Zhang, S., Shi, X., & Wu, C. (2017). Measuring the effects of external factor on leadership safety behavior: Case study of mine enterprises in China. Safety Science, 93, 241–255. https://doi.org/10.1016/j.ssci.2016.12.017
44. Zhang, Y., Shao, W., Zhang, M., Li, H., Yin, S., & Xu, Y. (2016). Analysis 320 coal mine accidents using structural equation modeling with unsafe conditions of the rules and regulations as exogenous variables. Accident Analysis & Prevention, 92, 189–201. https://doi.org/10.1016/j.aap.2016.02.021
45. Ziakkas, D., & Pechlivanis, K. (2023). Artificial intelligence applications in aviation accident classification: A preliminary exploratory study. Decision Analytics Journal, 9, 100358. https://doi.org/10.1016/j.dajour.2023.100358
Published
Issue
Section
License
Copyright (c) 2026 Irshad Ahmad, Ajay Kumar Guptab (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal operates under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, adaptation, and transformation in any medium, provided the original author and source are properly credited.
Authors retain the copyright of their articles.
The International Journal of Disaster Risk Management (IJDRM) encourages and permits authors to:
-
Post pre-print (submitted version), post-print (accepted version), and publisher’s version/PDF of their articles on personal websites, institutional repositories, disciplinary repositories, and academic networks such as ResearchGate, Academia.edu, or departmental websites,
-
Do so at any time, including before or after publication,
-
Provided that appropriate credit is given to the original publication in this journal, including:
-
Full bibliographic details
-
A clear mention of the journal name
-
A direct link to the article’s DOI (as an HTML link)
-
No prior permission is required from the publisher or editors for such actions, as long as the terms of the CC BY 4.0 license are followed.
