the evaluation of the functionality of artificial neural network and geostatistical methods in simulation of quality parameters of groundwater (case study: koohpayeh, isfahan)

groundwater resources are the most important sources of water consumption (agriculture, industry and drinking water) in arid and semi-arid regions. the management of these water resources is more expensive and difficult than surface waters; therefore more complicated and economic methods are needed for determination of their quality and quantity. in this study geostatistical methods of kringing and cokriging and multilayer perceptron (mlp) artificial neural network model were used to estimate the quality parameters of so42-, tds, ca and th. the methods were compared in order to understand which one is the best method for this estimation. data obtained from 50 wells located in koohpayeh plain in isfahan province was used for this study. for estimation of the functionality of these methods in simulation of the parameters, rmse and correlation coefficient were used. the results showed that for all of the studied parameters, mlp with a lower rmse and higher correlation coefficient showed the highest precision followed by cokriging. kriging showed to have the lowest precision in stimulation of the quality parameters.