Paper Information

Journal:   JOURNAL OF SCIENCE AND ENGINEERING ELITES   2019 , Volume 4 , Number 2 #a00482; Page(s) 267 To 281.

Investigating the Quality of Groundwater Resources for Agriculture, Drinking, Industry and Estimating Water Quality Parameters Using New Methods

Quality studies of groundwater have a special importance in the management of these resources. Therefore, it is important to evaluate and evaluate the quality of groundwater resources. Despite all the advances made in the science of water resources engineering, the problem of assessing the quality of groundwater to this day is the main problem observed in most of the plains of Iran. Therefore, it is important to manage and monitor the quality of water resources. In this research, we tried to predict and evaluate the quality of groundwater in Salmas Plain by using two models of artificial neural network RBF and GFF. To achieve this goal, groundwater qualitative data of Salmas plain was used during the 10 years period of 2002-2011. The results were evaluated based on Wilcox, Schuler and Piper standards. 70% of the available data was used to train the network and 10% of the data was used to validate the two models. Therefore, 20% of the remaining data was used to test the network. Using suitable and applicable statistical parameters showed that RBF model with Levenberg Marquardt training and 4 hidden layers has high potential for estimation and prediction of groundwater quality. Also, the correlation coefficient in this model is 0. 81 and root mean square error equal to 12. 23%. Also, the results of using different diagrams show that samples have a low hardness and corrosion. According to the classification of classes, most data are in the class C3S1. Based on the results, all of the water resources of the study area are acceptable to agriculture, drinking and industry, respectively.
Keyword(s): Agricultural uses,Water quality,Neural Network,RBF Model,GFF Model
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