Modeling quality parameters EC, SAR and TDS in groundwater using artificial neural network (case study: Mehran Plain and DEHLORAN)

Given the importance of ground water for drinking and agriculture sector, simulation and forecasting changes its quality is an increasing human needs. In this study, the modeling of water quality parameters TDS and EC based on other chemical components of the major anions and cations, SAR and pH have been carried out. In addition to modeling the sodium adsorption ratio as the dependent variable parameters latitude, electrical conductivity, total dissolved elements and pH values were used as independent variables. The neural network to predict Marquardt Levenberg- groundwater quality parameters were selected. Results showed that high performance neural network to predict the groundwater quality parameters. High levels of correlation coefficient obtained between the values of parameters modeled closely reflects anticipated the measured data and the ability and accuracy of the relationships between input variables with output. Coefficient of determination of all three elements were modeled in three phases: training, validation and testing is over 90 percent Which indicate acceptable accuracy and good learning neural network and efficient network using the learning algorithm and data provided to the network. The results of great importance for the planning and integrated management of water resources and conservation and better utilization of it is important in the study area.