[1] Singh, T.N. and Singh, V. (2005). An intelligent approach to predict and control ground vibration in mines. Geotech. Geol. Eng., 23(3), 249–262.
[2] Institute of Makers of Explosives (IME) (1997). Glossary of commercial explosives industry terms. Safety publication, No. 12, Institute of Makers of Explosives, Washington.
[3] Bajpayee, T.S., Rehak, T.R., Mowrey, G.L. and Ingram, D.K. (2002). A summary of fatal accidents due to flyrock and lack of blast area security in surface mining, 1989 to 1999. In: proceedings of the 28th annual conference on explosives and blasting technique, international society of explosives engineers (ISEE), Feb. 10-13, Las Vegas, pp 105–118.
[4] Rehak, T.R., Bajpayee, T.S., Mowrey, G.L. and Ingram, D.K. (2001). Flyrock issues in blasting. In: proceedings of the 27th annual conference on explosives and blasting technique, international society of explosives engineers (ISEE), Jan. 28-31, Orlando, pp 165–175.
[5] Raina, A.K., Murthy, V.M.S.R. and Soni, A.K. (2015). Flyrock in surface mine blasting: understanding the basics to develop a predictive regime. Current Science, 108(4), 660–665.
[6] Mishra, A.K. and Mallick, D.K. (2013). Analysis of blasting related accidents with emphasis on flyrock and its mitigation in surface mines. In: proceedings of rock fragmentation by blasting, Fragblast 10, Taylor and Francis, London, pp 555–561.
[7] Kecojevic, V. and Radomsky, M. (2005) Flyrock phenomena and area security in blasting-related accidents. Saf. Sci., 43(9), 739–750.
[8] Little, T.N. (2007). Flyrock risk. In: proceedings of EXPLO Conference, Sep. 3-4, Wollongong, pp 35–43.
[9] Verakis, H.C. and Lobb, T.E. (2003). An analysis of blasting accidents in mining operations. In: proceedings of 29th annual conference explosives and blasting technique, international society of explosives engineers (ISEE), Feb. 2-5, Nashville, pp 119–129.
[10] Ghasemi, E., Sari, M. and Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int. J. Rock Mech. Min. Sci., 52, 163–170.
[11] Raina, A.K., Murthy, V.M.S.R. and Soni, A.K. (2014). Flyrock in bench blasting: a comprehensive review. Bull. Eng. Geol. Environ., 73, 1199–1209.
[12] Monjezi, M., Bahrami, A. and Yazdian Varjani, A. (2010). Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. Sci., 47(3), 476–480.
[13] Rezaei, M., Monjezi, M. and Yazdian Varjani, A. (2011). Development of a fuzzy model to predict flyrock in surface mining. Saf. Sci., 49(2), 298–305.
[14] Monjezi, M., Amini Khoshalan, H. and Yazdian Varjani, A. (2012). Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab. J. Geosci., 5(3), 441–448.
[15] Amini, H., Gholami, R., Monjezi, M., Torabi, S.R. and Zadhesh, J. (2012). Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput. Appl., 21(8), 2077–2085.
[16] Khandelwal, K. and Monjezi, M. (2013). Prediction of flyrock in open pit blasting operation using machine learning method. Int. J. Min. Sci. Technol., 23(3), 313–316.
[17] Ghasemi, E., Amini, H., Ataei, M. and Khalokakaei, R. (2014). Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab. J. Geosci., 7(1), 193–202.
[18] Jahed Armaghani, D., Hajihassani, M., Tonnizam Mohamad, E., Marto, A. and Noorani, S.A. (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab. J. Geosci., 7(12), 5383–5396.
[19] Marto, A., Hajihassani, M., Jahed Armaghani, D., Tonnizam Mohamad, E. and Makhtar, A.M. (2014). A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci. World J., Article ID 643715.
[20] Trivedi, R., Singh, T.N. and Raina, A.K. (2014). Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J. Rock Mech. Geotech. Eng., 6(5), 447–454.
[21] Trivedi, R., Singh, T.N. and Gupta, N. (2015). Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech. Geol. Eng., 33(3), 875–891.
[22] Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., Alavi Nezhad Khalil Abad, S.V., Marto, A. and Moghaddam, M.R. (2016). Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng. Comput., 32(1), 109–121.
[23] Shirani Faradonbeh, R., Jahed Armaghani, D., Monjezi, M. and Tonnizam Mohamad, E. (2016). Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int. J. Rock Mech. Min. Sci., 88, 254–264.
[24] Yari, M., Bagherpour, R., Jamali, S. and Shamsi, R. (2016). Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety. Neural Comput. Appl., 27(3), 699–706.
[25] Raina, A.K. and Murthy, V.M.S.R. (2016). Importance and sensitivity of variables defining throw and flyrock in surface blasting by artificial neural network method. Current Science, 111(9), 1524–1531.
[26] Dehghani, H. and Shafaghi, M. (2017). Prediction of blast‑induced flyrock using differential evolution algorithm. Eng. Comput., 33(1), 149–158.
[27] Hudaverdi, T. and Akyildiz, O. (2017). A new classification approach for prediction of flyrock throw in surface mines. Bull. Eng. Geol. Environ.
[28] Quinlan, J.R. (1992). Learning with continuous classes. In: proceedings of the fifth Australian joint conference on artificial intelligence, world scientific, Singapore, pp 343–348.
[29] Wang, Y. and Witten, I.H. (1997). Induction of model trees for predicting continuous lasses. In: proceedings of the poster papers of the European conference on machine learning, Prague, Czech Republic.
[30] Ghasemi, E., Kalhori, H., Bagherpour, R. and Yagiz, S. (2018). Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks. Bull. Eng. Geol. Environ., 77(1), 331–343.
[31] Jung, N.-C., Popescu, I., Kelderman, P., Solomatine, D.P. and Price, R.K. (2010). Application of model trees and other machine learning techniques for algal growth prediction in Yongdam Reservoir, Republic of Korea. J. Hydroinform., 123, 262–274.
[32] WEKA (Waikato Environment for Knowledge Analysis), Version 3.6.12 (2014). The University of Waikato, Hamilton, New Zealand, available at: http://www.cs.waikato.ac.nz/ml/weka.
[33] Montgomery, D.C., Peck, E.A. and Vining, G.G. (2012). Introduction to linear regression analysis, 5th edn. Wiley, New Jersey.