:: Volume 4, Issue 4 (Autumn, 2017) ::
Environ. health eng. manag. 2017, 4(4): 225-231 Back to browse issues page
Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm
Meysam Alizamir , Soheil Sobhanardakani
Corresponding Author: Young Researchers & Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran , meysamalizamir@gmail.com
Abstract:   (1051 Views)
Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network(ANN) optimized by imperialist competitive algorithm (ICA).
Methods: This study presents a new method for predicting heavy metal concentrations in the groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data. Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE), were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy
metal concentrations can be reliably predicted by applying the new approach.
Results: Results from different statistical indicators during the training and validation periods indicate that the best performance can be obtained with the ANN-ICA model.
Conclusion: This method can be employed effectively to predict heavy metal concentrations in the groundwater resources of Ghahavand plain.
Keywords: Neural networks (computer), Groundwater, Models, Algorithms, Trace elements
eprint link: http://eprints.kmu.ac.ir/id/eprint/26832
Full-Text [PDF 876 kb]   (270 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/12/2 | Accepted: 2017/12/2 | Published: 2017/12/2



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Volume 4, Issue 4 (Autumn, 2017) Back to browse issues page