TY - JOUR ID - 40223 TI - Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach JO - Journal of Advances in Environmental Health Research JA - JAEHR LA - en SN - 2676-3478 AU - Alizamir, Meysam AU - Sobhanardakani, Soheil AD - Department of Civil Engineering, Young Researchers and Elite Club, Hamadan Branch, Islamic Azad University, Hamadan, Iran AD - Department of the Environment, School of Basic Sciences, Hamadan Branch, Islamic Azad University, Hamadan, Iran Y1 - 2016 PY - 2016 VL - 4 IS - 2 SP - 68 EP - 77 KW - Neural Networks KW - heavy metals KW - Groundwater KW - Forecasting KW - risk DO - 10.22102/jaehr.2016.40223 N2 - Nowadays 90% of the required water of Iran is secured with groundwater resources and forecasting of pollutants content in these resources is vital. Therefore, this research aimed to develop and employ the feedforward artificial neural network (ANN) to forecast the arsenic (As), lead (Pb), and zinc (Zn) concentration in groundwater resources of Asadabad plain. In this research, the ANN models were developed using MATLAB R2014 software program. The artificial intelligence models were trained with the data collected from field and then utilized as prediction tool. Levenberg-Marquardt (LM) and Bayesian regularization (BR) algorithms were employed as ANN training algorithms and their performance was evaluated using determination coefficient and the root mean square error. The results showed that the ANN models could potentially forecast heavy metals concentration in groundwater resources of the studied area. Coefficients of determination for ANN models for As, Pb and Zn in testing phase were 0.9288, 0.9823 and 0.8876, respectively. Finally, based on the simulation results, it was demonstrated that ANN could be applied effectively in forecasting the heavy metals concentration in groundwater resources of Asadabad plain.  UR - https://jaehr.muk.ac.ir/article_40223.html L1 - https://jaehr.muk.ac.ir/article_40223_257db8dd3e3290f6c7aee07a24dd7673.pdf ER -