Abstract
In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R, and Up three granular parameter sequences, according to the triangular fuzzy number; then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R, and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining, and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R, and Up sequences were 9.472 %, 16.671 %, and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity, and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.
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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
References
Akram M, Luqman A (2020) Granulation of ecological networks under fuzzy soft environment. Soft Comput 24(16):11867–11892
Ahmadi R, Shahrabi J, Aminshahidy B (2020) Forecasting multiple-well flow rates using a novel space-time modeling approach. J Petrol Sci Eng 191. https://doi.org/10.1016/j.petrol.2020.107027
Barman M, Choudhury NBD (2019) Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept. Energy 174:886–896
Bauer A, Züfle M, Herbst N, Zehe A, Hotho A, Kounev S (2020) Time series forecasting for self-aware systems. P IEEE 108(7):1068–1093
Carvalho FP (2017) Mining industry and sustainable development: time for change. Food Energy Secur 6(2):61–77
Dhiman HS, Deb D, Guerrero JM (2019) Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renew Sust Energy Rev 108:369–379
Gu B, Zhang T, Meng H, Zhang J (2021) Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation. Renew Energy 164:687–708
Haas EJ, Yorio P (2016) Exploring the state of health and safety management system performance measurement in mining organizations. Saf Sci 83:48–58
Huang Y, Zhou QY (2019) Mine accident prediction and analysis based on multimedia big data. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7175-6
Hassen OA, Darwish SM, Abu NA, Abidin ZZ (2020) Application of cloud model in qualitative forecasting for stock market trends. Entropy 22(9). https://doi.org/10.3390/e22090991
Joshi D, Sharma I, Gupta S, Singh TG, Dhiman S, Prashar A, Gulati M, Kumar B, Vishwas S, Chellappan DK (2021) A global comparison of implementation and effectiveness of materiovigilance program: overview of regulations. Environ Sci Pollut R 28(42):59608–59629
Li W, Ye YC, Wang QH, Wang XH, Hu NY (2020) Fuzzy risk prediction of roof fall and rib spalling: based on FFTA–DFCE and risk matrix methods. Environ Sci Pollut Res 27(8):8535–8547
Li XL, Cao ZY, Xu YL (2020) Characteristics and trends of coal mine safety development. Energy Source Part A. https://doi.org/10.1080/15567036.2020.1852339
Li XL, Chen SJ, Liu SM, Li ZH (2021) AE waveform characteristics of rock mass under uniaxial loading based on Hilbert-Huang transform. J Cent S Univ 28(6):1843–1856
Li XL, Chen SJ, Zhang QM, Gao X, Feng F (2021) Research on theory, simulation and measurement of stress behavior under regenerated roof condition. Geomech Eng 26(1):49–61
Lin YD, Li RM (2020) Real-time traffic accidents post-impact prediction: based on crowdsourcing data. Accid Anal Prev 145. https://doi.org/10.1016/j.aap.2020.105696
Luo C, Tan CH, Zheng YJ (2019) Long-term prediction of time series based on stepwise linear division algorithm and time-variant zonary fuzzy information granules. Int J Approx Reason 108:38–61
Luo ZQ, Li YY, Qin YG, Wen L (2020) A method developed for early warning of underground rock mass instability in mining area based on cusp catastrophe model and DS fusion evidence theory. Chin J Geo Hazard Control 31(5):60–69 [In Chinese] https://doi.org/10.16031/j.cnki.issn.1003-8035.2020.05.09
Mourenas D, Artemyev AV, Zhang XJ (2020) Dynamical properties of peak and time-integrated geomagnetic events inferred from sample entropy. J Geophys Res-Space 125(2). https://doi.org/10.1029/2019JA027599
Suhermi N, Prastyo DD (2018) Roll motion prediction using a hybrid deep learning and ARIMA model. Procedia Comput Sci 144:251–258
Stallone A, Cicone A, Materassi M (2020) New insights and best practices for the successful use of empirical mode decomposition, iterative filtering and derived algorithms. Sci Rep-UK 10(1) https://doi.org/10.1038/s41598-020-72193-2
Stemn E, Bofinger C, Cliff D, Hassall ME (2019) Examining the relationship between safety culture maturity and safety performance of the mining industry. Saf Sci 113:345–355
Verma S, Chaudhari S (2017) Safety of workers in Indian mines: study, analysis, and prediction. Saf Health Work 8(3):267–275
Wang YL, Li YM, Li C (2017) Prediction of coal mine gas accidents based on time series Markov model. Chin Min 26(12):179–183 ([in Chinese])
Wu ML, Ye YC, Hu NY, Wang QH, Li W, Jiang HM (2021) Interval prediction of mining work safety situation based on fuzzy information granulation. Chin Saf Sci J 31(09):119–127 ([in Chinese])
Wu ML, Ye YC, Hu NY, Wang QH, Jiang HM, Li W (2020) EMD-GM-ARMA model for mining safety production situation prediction. Complexity, 2020. https://doi.org/10.1155/2020/1341047
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The authors would like to thank editor and reviewers cordially for their positive and constructive suggestions.
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This work is supported by the National Natural Science Foundation of China (Grant No. 51704213) and the Key R & D projects in Hubei Province, China (No. 2020BCA082).
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WM is the executor of the modeling design and theoretical analysis of this study and is responsible for the writing of the first draft. YY, HN, and WQ completed data analysis and guided the writing and revision of the paper; TW participates in the modeling process and results analysis. All authors read and approved the final manuscript.
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Wu, M., Ye, Y., Hu, N. et al. Uncertainty prediction of mining safety production situation. Environ Sci Pollut Res 29, 64775–64791 (2022). https://doi.org/10.1007/s11356-022-20276-0
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DOI: https://doi.org/10.1007/s11356-022-20276-0