Use of Artificial Neural Network and Imperialist Competitive Algorithm to Evaluate the Groundwater Quality of Jolfa Plain for Various Uses

Document Type : Research Article


1 Water Engineering Department of Tabriz University

2 Water Engineering Department of Tabriz

3 Water Engineering Department of Gorgan of Gorgan University


Assessment of groundwater quality and quantity are important in the management of these resources. The use of modern methods, including ANN and evolutionary algorithms in estimating water quality, due to its high speed, convergence, and efficiency, saves and reduces costs and the best management. The main purpose of this study is to evaluate the results of the chemical analysis of groundwater samples from 14 wells in the Jolfa plain and also estimate the groundwater quality parameters using an imperialist competitive algorithm (ICA) and ANN. Therefore, groundwater quality parameters include TDS, EC, and SAR estimates using the imperialist competitive algorithm (ICA) and ANN, and groundwater resources quality in terms of drinking, agriculture, and industry were examined by Wilcox, Schuler, and Piper and standards. A correlation coefficient of (R2) 90%, indicates the acceptable accuracy of ANN compared with the ICA algorithm in estimating groundwater quality parameters. By using different diagrams the results show that the hardness of samples is too much and not suitable for drinking. It should also be noted that very high hardness and corrosion of sample, water not be used in industry. The salinity of 7 samples is very high and according to classification is located in the C4S2 class and not suitable for agricultural consumption.


Main Subjects

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