Document Type : Original Research Paper


1 Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

2 Department of Geology, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran


Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since several uncertain parameters are incorporated in the modelling process, distribution functions are employed to explain the parameters. However, due to the usual constrain of limited data, these functions cannot significantly explain the variation of those uncertain parameters. Support vector machine, one of the efficient techniques of artificial intelligence, provides the appropriate results in the classification and regression tasks. The principal aims of this research work are to integrate the simulation and artificial intelligence methods to manage the risk prediction of an economic system under uncertain conditions. The financial process of the Halichal mine in the Mazandaran province, Iran, is considered a case study to prove the performance of the support vector machine technique. The results show that integrating the simulation and support vector machine techniques can provide more realistic results, especially when including uncertain parameters. The correlation between the net present value obtained from the simulation and the net present value is about 0.96, which shows the capability of artificial intelligence methods and the simulation process. The root mean square error of the support vector machine prediction is about 0.322, which indicates a low error rate in the net present value estimation. The values of these errors prove that this method has a high accuracy and performance for predicting a net present value in the Halichal granite mine.


[1]. Aven, T. (2012). Foundations of risk analysis. John Wiley & Sons.
[2]. Lefley, F. (1997). Approaches to risk and uncertainty in the appraisal of new technology capital projects. International Journal of Production Economics. 53 (1): 21-33.
[3]. Corporation, P. (1994). Risk Analysis and Simulation Add-in for Microsoft Excel or Lotus 1-2-3: Windows Version Release 31 User’s Guide.
[4]. Behzad, M., Asghari, K., Eazi, M., and Palhang, M. (2009). Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with applications,. 36 (4): 7624-7629.
[5]. Guo, H., Nguyen, H., Vu, D. A., and Bui, X.N. (2021). Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Resources Policy, 74, 101474.
[6]. Fernandez, V. (2007). Wavelet-and SVM-based forecasts: An analysis of the US metal and materials manufacturing industry. Resources Policy. 32 (1-2): 80-89.
[7]. Avalos, S., Kracht, W., and Ortiz, J.M. (2020). Machine learning and deep learning methods in mining operations: A data-driven SAG mill energy consumption prediction application. Mining, Metallurgy & Exploration, 37(4): 1197-1212.
[8]. Alamdari, S., Basiri, M.H., Mousavi, A., and Soofastaei, A. (2022). Application of Machine Learning Techniques to Predict Haul Truck Fuel Consumption in Open-Pit Mines. Journal of Mining and Environment. 13 (1): 69-85.
[9]. Osuna, E., Freund, R., and Girosi, F. (1997, September). An improved training algorithm for support vector machines. In Neural networks for signal processing VII. Proceedings of the 1997 IEEE signal processing society workshop (pp. 276-285). IEEE.
[10]. Rajaraman, J.L. A., and Ullman, J.D. (2014). Mining of Massive Datasets: Advertising on the Web.
[11]. Platt, J. (1998). Using analytic QP and sparseness to speed training of support vector machines. Advances in neural information processing systems, 11.
[12]. Golewski, G.L. (2021). Green concrete based on quaternary binders with significant reduced of CO2 emissions. Energies. 14 (15): 4558.
[13]. Gil, D.M., and Golewski, G.L. (2018). Potential of siliceous fly ash and silica fume as a substitute for binder in cementitious concretes. In E3S Web of Conferences (Vol. 49, p. 00030). EDP Sciences.
[14]. Zhang, P., Han, S., Golewski, G.L., and Wang, X. (2020). Nanoparticle-reinforced building materials with applications in civil engineering. Advances in Mechanical Engineering. 12 (10): 1687814020965438.
[15]. Golewski, G.L. (2022). An extensive investigations on fracture parameters of concretes based on quaternary binders (QBC) by means of the DIC technique. Construction and Building Materials, 351, 128823.
[16]. Golewski, G.L. (2022). Comparative measurements of fracture toughgness combined with visual analysis of cracks propagation using the DIC technique of concretes based on cement matrix with a highly diversified composition. Theoretical and Applied Fracture Mechanics, 121, 103553.
[17]. Golewski, G.L. (2022). Fracture Performance of Cementitious Composites Based on Quaternary Blended Cements. Materials. 15 (17): 6023.
[18]. Gholami, R., and Moradzadeh, A. (2012). Support vector regression for prediction of gas reservoirs permeability. Journal of mining and environment. 2 (1).
[19]. Alimoradi, A., Moradzadeh, A., and Bakhtiari, M. R. (2013). Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data. Journal of Mining and Environment. 4 (1): 1-14.
[20]. Emami Meybodi, E., Hussain, S. K., Fatehi Marji, M., and Rasouli, V. (2022). Application of Machine Learning Models for Predicting Rock Fracture Toughness Mode-I and Mode-II. Journal of Mining and Environment. 13 (2): 465-480.
[21]. Sakizadeh, M., and Mirzaei, R. (2016). A comparative study of performance of K-nearest neighbors and support vector machines for classification of groundwater. Journal of Mining and Environment. 7 (2): 149-164.
[22]. Mahvash Mohammadi, N., and Hezarkhani, A. (2020). A comparative study of SVM and RF methods for classification of alteration zones using remotely sensed data. Journal of Mining and Environment. 11 (1): 49-61.
[23]. Celik, T., and Genc, B. (2021). A Comparative Study on Machine Learning Algorithms for Geochemical Prediction Using Sentinel-2 Reflectance Spectroscopy. Journal of Mining and Environment. 12 (4): 987-1001.
[24]. Mohamadnejad, M., Gholami, R., and Ataei, M. (2012). Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunnelling and Underground Space Technology, 28, 238-244.
[25]. Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
[26]. Lin, C.F., and Wang, S.D. (2005). Fuzzy support vector machines with automatic membership setting. In Support vector machines: Theory and applications (pp. 233-254). Springer, Berlin, Heidelberg.
[27]. Li, Q., Jiao, L., and Hao, Y. (2007). Adaptive simplification of solution for support vector machine. Pattern Recognition. 40 (3): 972-980.
[28]. Maleki, S., Ramazia, H. R., and Moradi, S. (2014). Estimation of Iron concentration by using a support vector machineand an artificial neural network-the case study of the Choghart deposit southeast of Yazd, Yazd, Iran. Geopersia, 4(2): 201-212.
[29]. Maleki, S., Moradzadeh, A., Riabi, R.G., Gholami, R., and Sadeghzadeh, F. (2014). Prediction of shear wave velocity using empirical correlations and artificial intelligence methods. NRIAG Journal of Astronomy and Geophysics. 3 (1): 70-81.
[30]. Maleki, S., Moradzadeh, A., Riabi, R. G., and Sadaghzadeh, F. (2014). Comparison of Several Different Methods of in situ stress determination. International Journal of Rock Mechanics and Mining Sciences, 71, 395-404.
[31]. Maleki, S., Moradzadeh, A., Ghavami, R., and Sadeghzadeh, F. (2013). A robust methodology for prediction of DT wireline log. Iranian Journal of Earth Sciences. 5 (1): 33-40.
[32]. Al-Anazi, A.F., and Gates, I.D. (2010). Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study. Computers & Geosciences. 36 (12): 1494-1503.
[33]. Schölkopf, B., Smola, A., and Müller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural computation. 10 (5): 1299-1319.
[34]. Agarwal, S., Saradhi, V.V., and Karnick, H. (2008). Kernel-based online machine learning and support vector reduction. Neurocomputing. 71 (7-9): 1230-1237.
[35]. Wu, C.H., Tzeng, G.H., and Lin, R.H. (2009). A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications, 36(3): 4725-4735.
[36]. Eryarsoy, E., Koehler, G. J., and Aytug, H. (2009). Using domain-specific knowledge in generalization error bounds for support vector machine learning. Decision Support Systems. 46 (2): 481-491.
[37]. Gunn, S.R. (1998). Support vector machines for classification and regression. ISIS technical report, 14(1): 5-16.
[38]. Lin, H.J., and Yeh, J.P. (2009). Optimal reduction of solutions for support vector machines. Applied Mathematics and Computation. 214 (2): 329-335.
[39]. Tran, Q.A., Li, X., and Duan, H. (2005). Efficient performance estimate for one-class support vector machine. Pattern Recognition Letters. 26 (8): 1174-1182.
[40]. Steinwart, I., and Christmann, A. (2008). Support vector machines. Springer Science & Business Media.
[41]. Li, Q., Jiao, L., and Hao, Y. (2007). Adaptive simplification of solution for support vector machine. Pattern Recognition. 40 (3): 972-980.
[42]. Kang-Lin, P., Wu, C.H., and Yeong-Jia, J.G. (2004). The development of a new statistical technique for relating financial information to stock market returns. International Journal of Management. 21 (4): 492.
[43]. Crider, J.G. (2001). Oblique slip and the geometry of normal-fault linkage: mechanics and a case study from the Basin and Range in Oregon. Journal of Structural Geology. 23 (12): 1997-2009.
[44]. Walczak, B., and Massart, D.L. (1996). The radial basis functions—partial least squares approach as a flexible non-linear regression technique. Analytica Chimica Acta. 331 (3): 177-185.
[45]. Wu, C.H., Tzeng, G.H., and Lin, R.H. (2009). A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications. 36 (3): 4725-4735.
[46]. Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.
[47]. Khandelwal, M. (2010). Evaluation and prediction of blast-induced ground vibration using support vector machine. International Journal of Rock Mechanics and Mining Sciences. 47 (3): 509-516.
[48]. Liu, H., Yao, X., Zhang, R., Liu, M., Hu, Z., and Fan, B. (2006). The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine. Chemosphere. 63 (5): 722-733.
[49]. Carlson, T.R., Erickson, J.D., O’Brain, D.T., and Pana, M.T. (1966). Computer techniques in mine planning. Mining Engineering. 18 (5): 53-56.
[50]. Slater, S.F., Reddy, V.K., and Zwirlein, T.J. (1998). Evaluating strategic investments: complementing discounted cash flow analysis with options analysis. Industrial Marketing Management. 27 (5): 447-458.
[51]. Ramasesh, R.V., and Jayakumar, M. D. (1997). Inclusion of flexibility benefits in discounted cash flow analyses for investment evaluation: A simulation/optimization model. European Journal of Operational Research. 102 (1): 124-141.
[52]. Samis, M., Davis, G.A., Laughton, D., and Poulin, R. (2005). Valuing uncertain asset cash flows when there are no options: A real options approach. Resources Policy. 30 (4): 285-298.
[53]. Dibike, Y.B., Velickov, S., Solomatine, D., and Abbott, M.B. (2001). Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering. 15 (3): 208-216.