Document Type: Original Research Paper


1 Department of Mining Engineering, University of Engineering and Technology Peshawar, Peshawar, Pakistan

2 School of Mines, China University of Mining and Technology, Xuzhou, China

3 Department of Mining Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan



In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surface and underground mining in Pakistan. The original application of the ARIMA model provides that how the number of accidents and fatalities is influenced by the implementation of various approaches to promote an effective safety management system. The ARIMA model requires the data series of the predicted elements with a random pattern over time and produce an equation. After the model identification, it may forecast the future pattern of the events based on its existing and future values. In this research work, the accident data for the period of 2006-2019-is collected from Inspectorate of Mines and Minerals (Pakistan), Mine Workers Federation, and newspapers in order to evaluate the long-term forecast. The results obtained reveal that ARIMA (2, 1, 0) is a suitable model for both the mining accidents and the workers’ fatalities. The number of accidents and fatalities are forecasted from 2020 to 2025. The results obtained suggest that the policy-makers should take a systematic consideration by evaluating the possible risks associated with an increased number of accidents and fatalities, and develop a safe and effective working platform. 


[1]. Malkani, M.S. and Mahmood, Z.A.F.A.R. (2016). Mineral resources of Pakistan: a review. Geological Survey of Pakistan, Record. 128: 1-90.

[2]. Malkani, M.S. and Mahmood, Z. (2017). Mineral Resources of Pakistan: provinces and basins wise. Geological Survey of Pakistan, Memoir. 25: 1-179.

[3]. Malkani, M.S., Mahmood, Z., Alyani, M.I. and Siraj, M. (2017). Mineral Resources of Khyber Pakhtunkhwa and FATA, Pakistan. Geological Survey of Pakistan, Information Release. 996: 1-61.

[4]. kausar sultan shah, S.k., Abdur Rehman, Socio-Environmental Impacts of Coal Mining: A Case Study of Cherat Coal Mines Pakistan. Int. J. Econ. Environ. Geol. Vol., 2019. 10 (3): p. 129-133.

[5]. Shah, K.S., Khan, M.A., Khan, S., Rahman, A., Khan, N.M. and Abbas, N. (2020). Analysis of Underground Mining Accidents at Cherat Coalfield, Pakistan. International Journal of Economic and Environmental Geology. 11 (1): 113-117.

[6]. Jiskani, I. M., Cai, Q., Zhou, W. and Lu, X. (2020). Assessment of risks impeding sustainable mining in Pakistan using fuzzy synthetic evaluation. Resources Policy. 69: 101820.

[7]. Jiskani, I.M., Ullah, B., Shah, K.S., Bacha, S., Shahani, N.M., Ali, M. and Qureshi, A.R. (2019). Overcoming mine safety crisis in Pakistan: An appraisal. Process safety progress. 38 (4): e12041.

[8]. Jiskani, I.M., Cai, Q., Zhou, W., Chang, Z., Chalgri, S.R., Manda, E. and Lu, X. (2020). Distinctive Model of Mine Safety for Sustainable Mining in Pakistan. Mining, Metallurgy & Exploration, 1-15.

[9]. Zhang, J., Fu, J., Hao, H., Fu, G., Nie, F. and 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.

[10]. Shahani, N.M., Sajid, M.J., Jiskani, I.M., Ullah, B. and Qureshi, A.R. (2020). Comparative analysis of coal Miner’s fatalities by fuzzy logic. Journal of Mining and Environment.

[11]. Bonsu, J., Van Dyk, W., Franzidis, J.P., Petersen, F. and Isafiade, A. (2017). A systemic study of mining accident causality: an analysis of 91 mining accidents from a platinum mine in South Africa. Journal of the Southern African Institute of Mining and Metallurgy. 117 (1): 59-66.

[12]. Sarkar, S., Vinay, S., Raj, R., Maiti, J. and Mitra, P. (2019). Application of optimized machine learning techniques for prediction of occupational accidents. Computers & Operations Research, 106, 210-224.

[13]. Wang, R., Xu, K., Xu, Y. and Wu, Y. (2020). Study on prediction model of hazardous chemical accidents. Journal of Loss Prevention in the Process Industries, 104183.

[14]. Xie, X., Fu, G., Xue, Y., Zhao, Z., Chen, P., Lu, B. and Jiang, S. (2019). Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention. Process Safety and Environmental Protection. 122: 169-184.

[15]. Xu, Q. and Xu, K. (2020). Statistical analysis and prediction of fatal accidents in the metallurgical industry in China. International journal of environmental research and public health, 17 (11): 3790.

[16]. Box, G.E., et al., Time series analysis: forecasting and control. 2015: John Wiley & Sons.

[17]. Kher, A.A. and Yerpude, R. (2016). Application of Forecasting Models on Indian Coal Mining Fatal Accident (Time Series) Data. International Journal of Applied Engineering Research, 11(2), 1533-1537.

[18]. Al-Zyood, M. (2017). Forecast car accident in Saudi Arabia with ARIMA models. International Journal of Soft Computing and Engineering, 7 (3): 30, 33.

[19]. Li, Y. (2019). Analysis and Forecast of Global Civil Aviation Accidents for the Period 1942-2016. Mathematical Problems in Engineering, 2019.

[20]. Ghédira, A., Kammoun, K. and Saad, C.B. (2018). Temporal Analysis of Road Accidents by ARIMA Model: Case of Tunisia. International Journal of Innovation and Applied Studies. 24 (4): 1544-1553.

[21]. Rajaprasad, S.V.S. (2018). Prediction of fatal accidents in Indian factories based on ARIMA. Production Engineering Archives. 18 (18): 24-30.

[22]. Wu, M., Ye, Y., Hu, N., Wang, Q., Jiang, H. and Li, W. (2020). EMD-GM-ARMA Model for Mining Safety Production Situation Prediction. Complexity.

[23]. Shao, X., Boey, L.L. and Luo, Y. (2019). Traffic Accident Time Series Prediction Model Based on Combination of ARIMA and BP and SVM. Journal of Traffic and Logistics Engineering Vol, 7(2).

[24]. Yixuan, S.U.N., Chunfu, S.H.A.O., Xun, J. I. and Liang, Z.H.U. (2015). Urban traffic accident time series prediction model based on combination of ARIMA and information granulation SVR. Journal of tsinghua university (science and technology). 54 (3): 348-353.

[25]. Shumway, R. and D. Stoffer. (2011). ARIMA models’, Time Series Analysis and Its Applications. Springer New York, NY, USA.