Shahrood University of Technology Journal of Mining and Environment 2251-8592 8 1 2017 01 01 Use of artificial intelligence techniques to predict distribution of heavy metals in groundwater of Lakan lead-zinc mine in Iran 35 48 592 10.22044/jme.2016.592 EN Z. Bayatzadeh Fard Department of Mining Engineering, Arak University of Technology, Arak, Iran F. Ghadimi Department of Mining Engineering, Arak University of Technology, Arak, Iran H. Fattahi Department of Mining Engineering, Arak University of Technology, Arak, Iran. Journal Article 2016 01 10 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. https://jme.shahroodut.ac.ir/article_592_250d06888116ca1fee0f7cf3c334ee01.pdf