Advancing Safety in Mining: Machine Learning Approaches for Predicting and Classifying Seismic Bump-Associated Hazardous States

Fei  Li

doi:10.6180/jase.202507_28(7).0012

Advancing Safety in Mining: Machine Learning Approaches for Predicting and Classifying Seismic Bump-Associated Hazardous States

Civil Engineering Environmental Engineering

Fei Li

School of Zhengzhou University of Light Industry; Zhengzhou Henan, 450000, China

Received: May 20, 2024
Accepted: September 1, 2024
Publication Date: October 7, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202507_28(7).0012

The foundation and presumption of underlying risk management in underground coal mines is hazard identification. Even though hazard identification techniques used in underground coal mines have been extensively studied, there is still room for improvement. Because they are experience-based or limited to a single incident or event, traditional hazard identification techniques lack a systematic and all-encompassing identification framework. The material offered explores the intricate problem of predicting high-energy seismic bumps in coal mines that are more than 10⁴ Joules. The study uses 2 single predictive models (Random Forest (RF) and Support Vector Classification (SVC)) along with 2 optimization strategies (Artificial Hummingbird Algorithm (AHA) and Turbulent Flow of Water-based Optimization Algorithm (TFWOA)) to tackle this problem. These techniques are applied to improve forecast accuracy. Once the dataset has been divided into hazardous groups and those that are not, a careful analysis of the numerical results is carried out. After a thorough analysis, the most efficient model is the RFC + TFWOA (RFTF) model, which uses Random Forest Classification (RFC) optimized by Turbulent Flow of Water-based Optimization. Notably, the RFTF model attains an astounding accuracy of 0.898 throughout the training phase. This result demonstrates that the RFTF model is more effective than other models at correctly classifying states as hazardous or non-hazardous.

Keywords: Risk Management; Seismic Hazard Prediction; Underground Coal Mines; Hazardous and Non-Hazardous States Classification.

[1] W. Gale, K. Heasley, A. Iannacchione, P. Swanson, P. Hatherly, and A. King. “Rock damage characterisation from microseismic monitoring”. In: ARMA US Rock Mechanics/Geomechanics Symposium. ARMA. 2001, ARMA–01.
[2] S. J. Gibowicz and S. Lasocki, (2001) “Seismicity induced by mining: Ten years later" Advances in geophysics 44: 39–181. DOI: 10.1016/S0065-2687(00)80007-2.
[3] A. Cianciara and B. Cianciara, (2006) “The meaning of seismoacoustic emission for estimation of time of mining tremors occurrence" Archives of Mining Sciences 51: 563–575.
[4] A. Le´sniak and Z. Isakow, (2009) “Space–time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland" International Journal of Rock Mechanics and Mining Sciences 46: 918–928. DOI: 10.1016/j.ijrmms.2008.12.003.
[5] G. Van Aswegen. “Routine seismic hazard assessment in some South African mines”. In: Proceedings of the Sixth International Symposium on Rockbursts & Seismicity in Mines, Y. Potvin and M. Hudyma (eds), Australian Centre for Geomechanics. 2005, 437–444. DOI: 10.36487/ACG_repo/574_45.
[6] S. Lasocki. “Probabilistic analysis of seismic hazard posed by mining induced events”. In: Proc. 6th Int. Symp. on Rockburst in Mines “Controlling Seismic Risk”. ACG, Perth. 2005, 151–156. DOI: 10.36487/ACG_repo/ 574_11.
[7] J. Kornowski, (2003) “Linear prediction of aggregated seismic and seismoacoustic energy emitted from a mining longwall" Acta Montana 22: 5–14.
[8] R. D. Lama and J. Bodziony, (1998) “Management of outburst in underground coal mines" International Journal of Coal Geology 35: 83–115. DOI: 10.1016/S0166-5162(97)00037-2.
[9] M. Sari, H. S. B. Duzgun, C. Karpuz, and A. S. Selçuk, (2004) “Accident analysis of two Turkish underground coal mines" Safety Science 42: 675–690. DOI: 10.1016/j.ssci.2003.11.002.
[10] H. S. B. Duzgun and H. H. Einstein, (2004) “Assessment and management of roof fall risks in underground coal mines" Safety Science 42: 23–41. DOI: 10.1016/S0925-7535(02)00067-X.
[11] H. S. B. Düzgün, (2005) “Analysis of roof fall hazards and risk assessment for Zonguldak coal basin underground mines" International Journal of Coal Geology 64: 104–115. DOI: 10.1016/j.coal.2005.03.008.
[12] R. L. Grayson, H. Kinilakodi, and V. Kecojevic, (2009) “Pilot sample risk analysis for underground coal mine fires and explosions using MSHA citation data" Safety science 47: 1371–1378. DOI: 10.1016/j.ssci.2009.03.004.
[13] J. Maiti and V. V. Khanzode, (2009) “Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India" Safety science 47: 1068–1076. DOI: 10.1016/j.ssci.2008.12.003.
[14] P. S. Paul, (2009) “Predictors of work injury in underground mines—an application of a logistic regression model" Mining Science and Technology (China) 19: 282–289. DOI: 10.1016/S1674-5264(09)60053-3.
[15] K. Shahriar and E. Bakhtavar, (2009) “Geotechnical risks in underground coal mines" Journal of Applied Sciences 9: 2137–2143. DOI: 10.3923/jas.2009.2137.2143.
[16] V. V. Khanzode, J. Maiti, and P. K. Ray, (2011) “A methodology for evaluation and monitoring of recurring hazards in underground coal mining" Safety Science 49: 1172–1179. DOI: 10.1016/j.ssci.2011.03.009.
[17] A. Nieto, Y. Gao, L. Grayson, and G. Fu, (2014) “A comparative study of coal mine safety performance indicators in China and the USA" International Journal of Mining and Mineral Engineering 5: 299–314. DOI: 10.1504/IJMME.2014.066578.
[18] Q. Liu, X. Li, and X. Meng, (2019) “Effectiveness research on the multi-player evolutionary game of coal-mine safety regulation in China based on system dynamics" Safety science 111: 224–233. DOI: 10.1016/j.ssci.2018.07.014.
[19] Q. Liu, X. Li, and X. Meng, (2019) “Effectiveness research on the multi-player evolutionary game of coal-mine safety regulation in China based on system dynamics" Safety science 111: 224–233. DOI: 10.1016/j.ssci.2018.07.014.
[20] S. Mahdevari, K. Shahriar, and A. Esfahanipour, (2014) “Human health and safety risks management in underground coal mines using fuzzy TOPSIS" Science of the Total Environment 488: 85–99. DOI: 10.1016/j.scitotenv.2014.04.076.
[21] A. Nieto, Y. Gao, L. Grayson, and G. Fu, (2014) “A comparative study of coal mine safety performance indicators in China and the USA" International Journal of Mining and Mineral Engineering 5: 299–314. DOI: 10.1504/IJMME.2014.066578.
[22] Q. Liu, X. Li, and M. Hassall, (2015) “Evolutionary game analysis and stability control scenarios of coal mine safety inspection system in China based on system dynamics" Safety science 80: 13–22. DOI: 10.1016/j.ssci.2015.07.005.
[23] S. Mahdevari, K. Shahriar, and A. Esfahanipour, (2014) “Human health and safety risks management in underground coal mines using fuzzy TOPSIS" Science of the Total Environment 488: 85–99. DOI: 10.1016/j.scitotenv.2014.04.076.
[24] Q. Liu, X. Li, and M. Hassall, (2015) “Evolutionary game analysis and stability control scenarios of coal mine safety inspection system in China based on system dynamics" Safety science 80: 13–22. DOI: 10.1016/j.ssci.2015.07.005.
[25] V. Rudajev and R. Ciž, (1999) ˇ “Estimation of mining tremor occurrence by using neural networks" Pure and applied geophysics 154: 57–72. DOI: 10.1007/s000240050221.
[26] J. Kabiesz, (2006) “Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks" Geotechnical Geological Engineering 24: 1131–1147. DOI: 10.1007/s10706-005-1136-8.
[27] B. Bodri, (2001) “A neural-network model for earthquake occurrence" Journal of Geodynamics 32: 289–310. DOI: 10.1016/S0264-3707(01)00039-4.
[28] I. U. Sikder and T. Munakata, (2009) “Application of rough set and decision tree for characterization of premonitory factors of low seismic activity" Expert Systems with Applications 36: 102–110. DOI: 10.1016/j.eswa.2007.09.032.
[29] C. Liu, M. White, and G. Newell. “Measuring the accuracy of species distribution models: a review”. In: Proceedings 18th World IMACs/MODSIM Congress. Cairns, Australia. 4241. 2009, 4247.
[30] S. K. Ghosh and F. Janan. “Prediction of student’s performance using random forest classifier”. In: Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management, Singapore. 2021, 7–11.
[31] L. Breiman, (2001) “Random forests" Machine learning 45: 5–32. DOI: 10.1023/A:1010933404324.
[32] V. Vapnik, (1998) “Statistical Learning Theory. New York: John Willey Sons" Inc:
[33] S. Maldonado, J. Pérez, R. Weber, and M. Labbé, (2014) “Feature selection for support vector machines via mixed integer linear programming" Information sciences 279: 163–175. DOI: 10.1016/j.ins.2014.03.110.
[34] C.-C. Chang and C.-J. Lin, (2011) “LIBSVM: a library for support vector machines" ACM transactions on intelligent systems and technology (TIST) 2: 1–27. DOI: 10.1145/1961189.1961199.
[35] J. Wang, Y. Li, G. Hu, and M. Yang, (2022) “An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization" Advanced Engineering Informatics 54: 101761. DOI: 10.1016/j.aei.2022.101761.
[36] W. Zhao, L. Wang, and S. Mirjalili, (2022) “Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications" Computer Methods in Applied Mechanics and Engineering 388: 114194. DOI: 10.1016/j.cma.2021.114194.
[37] A. Ramadan, S. Kamel, M. H. Hassan, E. M. Ahmed, and H. M. Hasanien, (2022) “Accurate photovoltaic models based on an adaptive opposition artificial hummingbird algorithm" Electronics 11: 318. DOI: 10.3390/electronics11030318.
[38] M. S. Abid, H. J. Apon, K. A. Morshed, and A. Ahmed, (2022) “Optimal planning of multiple renewable energy-integrated distribution system with uncertainties using artificial hummingbird algorithm" IEEE Access 10: 40716–40730. DOI: 10.1109/ACCESS.2022.3167395.
[39] W. Zhao, Z. Zhang, S. Mirjalili, L. Wang, N. Khodadadi, and S. M. Mirjalili, (2022) “An effective multiobjective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems" Computer Methods in Applied Mechanics and Engineering 398: 115223. DOI: 10.1016/j.cma.2022.115223.
[40] A. Fathy, (2022) “A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems" Applied Energy 323: 119605. DOI: 10.1016/j.apenergy. 2022.119605.