Document Type : Case Study
Authors
1 Department of Mining Engineering, Arak University of Technology, Arak, Iran
2 Department of Mining Engineering, Arak University of Technology, Arak, Iran.
Abstract
Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization (ANN-BBO), and multi-output adaptive neural fuzzy inference system (MANFIS) to estimate the distribution of heavy metals in groundwater of Lakan lead-zinc mine is demonstrated.For this purpose, the contamination groundwater resources were determined using the existing groundwater quality monitoring data, and several models were trained and tested using the collected data to determine the optimum model that used three inputs and four outputs. A comparison between the predicted and measured data indicated that the MANFIS model had the mostpotential to estimate the distribution of heavy metals in groundwater with a high degree of accuracy and robustness.
Keywords
[1]. Moncur, M.C., Ptacek, C.J, Blowes, D.W. and Jambor, J.L. (2005). Release, transport and attenuation of metals from an old tailings impoundment. Appl Geochem. 20: 639-659.
[2].Rogers, L.L. and Dowla, F.U. (1994). Optimization Of groundwater mediation using artificial neural networks with parallel solute transport modeling, Water Res. Res. 30 (2): 457-481.
[3].Schleiter, L.M., Borchardt, D., Wagner, R.T., Dapper, T., Schmidt, K.D. and Schmidt, H.H. (1999). Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks. Ecological Modelling. 120: 271-286.
[4].Gizogluj, H.K. (2002). Suspended Sediment Estimation for Rivers using Artifcial Neural Networks and Sediment Rating Curves. Turkish J.Eng Env Sci. 26: 27-36.
[5].Kemper, T. and Sommer, S. (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Techno. l36: 2742-2747.
[6].Liu, C.W., Kuo, Y.M. and Lin, K.H. (2004). Evaluation of the ability of an artificial neural networkmodel to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Research. 38: 148-158.
[7].Almasri, M.N. and Kaluarach, J.J. (2005). Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environmental Modelling & Software. 20:851-871.
[8]. Palani, S., Liong, S.Y. and Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin. 56: 1586-1597.
[9]. Noori, R., Khakpour, A., Omidvar, B. and Farokhnia, A. (2010). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications. 37: 5856-5862.
[10]. Rooki, R., Doulati Ardejani, F., Aryafar, A. and Bani Asadi, A. (2011). Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran. Environ Earth Sci: DOI 10.1007/s12665-011-0948-5.
[11]. Heydar, M., Olyaie, E., Mohebzadeh, H. and Kisi, O. (2013). Development of a Neural Network Technique for Prediction of Water Quality Parameters in the Delaware River, Pennsylvania. Middle-East Journal of Scientific Research. 13 (10): 1367-1376.
[12]. Badaoui, H.E., Abdallaoui, A., Manssouri, I. and Lancelot, L. (2013). Application of the Artificial Neural Networks of MLP Type for the Prediction of the Levels of Heavy Metals in Moroccan Aquatic Sediments. International Journal of Computational Engineering Research. 3 (6):75-81.
[13].Irfan Yesilnacar, M. and Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey,Environmental Earth Sciences. 67(4): 1111-1119.
[14]. Keskin, T., Düğenci, M. and Fikrt Kaçaroğlu, F. (2015). Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey).Environmental Earth Sciences. 73 (9) 5333-5347.
[15].Nasr, M. and Zahran, H.F. (2014). Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. Egyptian Journal of Aquatic Research. 49 (2): 111-115.
[16]. Grande, J.A., Andujar, J.M., Aroba, J. and Torre, M.L. (2009). Presence of As in the fluvial network due to AMD processes in the Riotinto mining area (SW Spain): A fuzzy logic qualitative model. Journal of Hazardous Materials. 176: 395-401.
[17]. Yan Zou, H.Z. and Wang. H. (2010). Adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences. 22 (12): 1891-1896.
[18]. Sahu, H.B., Padhee, S. and Mahapatra, S.S. (2010). Prediction of spontaneous heating susceptibility of Indian coals using fuzzy logic and artificial neural network models. Expert Systems with Applications. 38: 2271-2282.
[19]. Valente, T., Ferreira, M.J., Grande, J.A., Torre, M.L.D.L. and Borrego, J. (2013). PH, electric conductivity and sulfate as base parameters to estimate the concentration of metals in AMD using a fuzzy inference system. Journal of Geochemical Exploration. 124: 22-28.
[20]. Mohammadi, L. and Meech, J.A. (2012). AFRA - Heuristic expert system to assess the atmospheric risk of sulphide waste dumps. Journal of Loss Prevention in the Process Industries. 26: 261-271.
[21]. Zhang, H., Song, J., Su, C. and He, M. (2012). Human attitudes in environmental management: Fuzzy Cognitive Maps and policy option simulations analysis for a coal-mine ecosystem in China. Journal of Environmental Management. 115: 227-234.
[22]. Liu, D. and Zou, Z. (2012). Water quality evaluation based on improved fuzzy matter-element method. Journal of Environmental Sciences. 24 (7): 1210-1216.
[23].Pourjabbar, A., Sârbu, C., Kostarelos, K., Einax, J.W. and Büchel, G. (2014). Fuzzy hierarchical cross-clustering of data from abandoned mine site contaminated with heavy metals. Computers & Geosciences. 72: 122-133.
[24]. Mahdevari, S., Shahriar, K. and Esfahanipour. A. (2014). Human health and safety risks management in underground coal mines using fuzzy TOPSIS. Science of the Total Environment. 488-489: 85-99.
[25]. Chang, F.J., Chung, C.H., Chen, P.A., Liu, C.W., Coynel, A. and Vachaud, G. (2014). Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis. Science of the Total Environment. 494-495: 202-210.
[26]. Maiti, S. and Tiwari, R. K. (2014). A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction.Environmental Earth Sciences. 71 (7): 3147-3160.
[27]. Ghadimi, F. (2015). Prediction of heavy metals contamination in the groundwater of Arak region using artificial neural network and multiple linear regression. Journal of Tethys. 3 (3): 203-215.
[28]. GSI. (2007). Geological map of Varcheh Khomain. Geological Society of Iran.
[29]. Ghadimi, F., Ghomi, M. and Hajati, A. (2012). Identification of groundwater contamination sources of Lakan lead and zinc mine, Khomain, Iran,.Journal of Mining and Environment. 3 (2): 121-134.
[30]. Jorjani, E., Chehreh Chelgani, S. and Mesroghli, S.H. (2008). Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel. 87: 2727-2734.
[31]. Yao, H.M., Vuthaluru, H.B. and Tade, M.O. (2005). Djukanovic D. Artificial neural networkbased prediction of hydrogen content of coal in power station boilers. Fuel. 84: 1535-42.
[32]. Hajihassani, M., Jahed Armaghani, D., Sohaei, H., Tonnizam Mohamad, E. and Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust. 80: 57-67.
[33]. Simon, D. (2008). Biogeography-based Optimization. IEEE Trans. Evol. Comput. 12 (6): 702-713.
[34]. Zadeh, L.A. (1965). Fuzzy sets. Inform Control. 8: 338-353.
[35]. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybernet. 15: 116-32.
[36]. Wesley, J. (1997). Fuzzy and Neural Approaches in Engineering. Hines New York.
)