TY - JOUR ID - 592 TI - Use of artificial intelligence techniques to predict distribution of heavy metals in groundwater of Lakan lead-zinc mine in Iran JO - Journal of Mining and Environment JA - JME LA - en SN - 2251-8592 AU - Bayatzadeh Fard, Z. AU - Ghadimi, F. AU - Fattahi, H. AD - Department of Mining Engineering, Arak University of Technology, Arak, Iran AD - Department of Mining Engineering, Arak University of Technology, Arak, Iran. Y1 - 2017 PY - 2017 VL - 8 IS - 1 SP - 35 EP - 48 KW - Groundwater KW - ANN KW - MANFIS KW - Heavy Metals KW - Biogeography-Based Optimization Algorithm DO - 10.22044/jme.2016.592 N2 - 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. UR - https://jme.shahroodut.ac.ir/article_592.html L1 - https://jme.shahroodut.ac.ir/article_592_250d06888116ca1fee0f7cf3c334ee01.pdf ER -