Prediction of toxic metals concentration using artificial intelligence techniques

R. Gholami 0 A. Kamkar-Rouhani 0 F. Doulati Ardejani 0 Sh. Maleki 0 0 R. Gholami (&) A. Kamkar-Rouhani F. Doulati Ardejani Sh. Maleki Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology , Shahrood, Iran Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN. - Copper exploitation causes a major water quality problem due to acid mine drainage (AMD) generation in Sarcheshmeh mine, Kerman Province, southeast Iran. The oxidation of sulphide minerals particularly pyrite exposed to atmospheric oxygen during or after mining activities generates acidic waters with high concentrations of dissolved iron (Fe), sulphate (SO4) and both of the heavy and toxic metals (Williams 1975; Moncur et al. 2005). The low pH of AMD may cause further dissolution and the leaching of additional metals (Mn, Zn, Cu, Cd, and Pb) into aqueous system (Zhao et al. 2007). AMD containing heavy and toxic metals has detrimental impact on aquatic life and the surrounding environment. Shur River in the Sarcheshmeh copper mine area is polluted by AMD with pH values ranging between 2 and 4.5 and high concentrations of heavy and toxic metals. The prediction of toxic metals in Shur River is useful in developing proper remediation and monitoring methods. Environmental problems due to the oxidation of sulphide minerals and hence AMD generation in the Sarcheshmeh copper mine and its impact on the Shur River have been investigated in the past (Marandi et al. 2007; Shahabpour and Doorandish 2008; Doulati Ardejani et al. 2008; Bani Assadi et al. 2008; Derakhshandeh and Alipour 2010). In addition, several investigations have been carried out using artificial neural networks (ANN) multiple linear regression (MLR) in different fields of environmental engineering in the past few decades (Karunanithi et al. 1994; Lek and Guegan 1999; Govindaraju 2000; Karul et al. 2000; Bowers and Shedrow 2000; Kemper and Sommer 2002; Dedecker et al. 2004; Kuo et al. 2004, 2007; Khandelwal and Singh 2005 Almasri and Kaluarachchi 2005; Kurunc et al. 2005; Sengorur et al. 2006; Messikh et al. 2007; Palani et al. 2008; Hanbay et al. 2008; Chenard and Caissie 2008; Dogan et al. 2009; Singh et al. 2009). Recently, a novel machine learning technique, called support vector machine (SVM), has drawn much attention in the fields of pattern classification and regression forecasting. SVM was first introduced by Vapnik (1995). SVM is a kind of classification methods on statistic study theory. This algorithm derives from linear classifier, and can solve the problem of two-kind classifier, later this algorithm applies in non-linear fields, i.e. to say, we can find the optimal hyperplane (large margin) to classify the samples set. It is an approximate implementation to the structure risk minimization (SRM) principle in statistical learning theory (SLT), rather than the empirical risk minimization (ERM) method (Kwok 1999). Compared with traditional neural networks, SVM can use the theory of minimizing the structure risk to avoid the problems of excessive study, calamity data, local minimal value and etc. For the small samples set, this algorithm can be generalized well. SVM has been successfully used for machine learning with large and highdimensional datasets. These attractive properties make SVM become a promising technique. This is due to the fact that the generalization property of a SVM does not depend on the complete training data but only a subset, the so-called support vectors. Now, SVM has been applied in many fields as follows: handwriting recognition, three-dimension objects recognition, faces recognition, text images recognition, voice recognition, regression analysis, and so on (Carbonneau et al. 2008; Chen and Hsieh 2006; Huang 2008; Seo 2007; Trontl et al. 2007; Wohlberg et al. 2006). The aim of this paper is to predict the concentration of two toxic metals namely Fe and Ni using SVM. For making a good comparison, the obtained results will be compared with those given by a back-propagation neural network (BPNN). Sarcheshmeh copper mine is located 160 km to southwest of Kerman and 50 km to southwest of Rafsanjan in Kerman province, Iran. The main access road to the study area is Kerman-Rafsanjan-Shahr Babak road. This mine belongs to Band Mamazar-Pariz Mountains. The average elevation of the mine is 1,600 m. The mean annual precipitation of the mine area varies from 300 to 550 mm. The temperature varies from ?35 C in summer to -20 C in winter. The area is covered with snow about 34 months per year. The wind speed sometimes exceeds to 100 km/h. A rough topography is predominant at the mining area. Figure 1 shows the geographical position of the Sarcheshmeh copper mine. The orebody in Sarcheshmeh is oval shaped with a long dimension of about 2,300 m and a width of about 1,200 m. This deposit is associated with the late Tertiary Sarcheshmeh granodiorite porphyry stock (Waterman and Hamilton 1975). The porphyry is a member of a complex series of magmatically related intrusives emplaced in the Tertiary volcanics at a short distance from the edge of an older near-batholith-sized granodiorite mass. Open pit mining method is used to extract copper ore in the Sarcheshmeh mine. A total of 40,000 tons of ore (average grades 0.9% Cu and 0.03% molybdenum) are approximately extracted per day from the Sarcheshmeh mine (Banisi and Finch 2001). Sampling and field methods Sampling of water in the Shur River downstream from the Sarcheshmeh mine was carried out in February 2006. Water samples consist of water from the Shur River (Fig. 1) originating from the Sarcheshmeh mine, acidic leachates of heap structure, run-off of leaching solution into the River and tailings along the Shur River. The water samples were immediately acidified by adding HNO3 (10 cc acid to 1,000 cc sample) and stored under cool conditions. The equipments used in this study consisted of sample container, GPS, oven, autoclave, pH meter, atomic adsorption and ICP analysers. The pH of the water samples was measured using a portable pH meter in the field. Other field measured quantities were total dissolved solids (TDS), electric conductivity (EC) and temperature. Analyses for dissolved metals were performed usin (...truncated)