%0 Journal Article
%T Sodium Adsorption Ratio (SAR) Prediction of the Chalghazi River Using Artificial Neural Network (ANN) Iran
%A Gholamreza Asadollahfardi
%A Azadeh Hemati
%A Saber Moradinejad
%A Rashin Asadollahfardi
%J Current World Environment
%D 2013
%I Enviro Research Publishers
%R http://dx.doi.org/10.12944/cwe.8.2.02
%X Considering the significance of the Sodium Adsorption Ratio (SAR) for growing plants, its prediction is essential for water quality management for irrigation. The SAR prediction in Chelghazy River in Kurdistan, northwest of Iran, using an Artificial Neural Network (ANN) was studied. The study applied the Multilayer Perceptron (MLP) of the ANN to average monthly data, which was collected by the water authority of the Kurdistan province for the period of 1998-2009. The input parameters of the MLP network was pH, discharge, sulfate, sodium, calcium, chloride, magnesium and bicarbonate, and output was predictive of the SAR. The results showed a correlation coefficient 0.976 between actual and predicted SAR, which means the accuracy of the model is acceptable. The model uses the input parameters to predict the SAR at the same month. The sensitivity analysis indicated the prediction of the SAR was affected by merely pH and calcium. As a whole, the MLP of the ANN may be applicable for prediction of the SAR which is necessary parameter ration for agriculture.
%K Artificial neural network (ANN)
%K Sodium Adsorption Ratio (SAR)
%K Chalghazi River
%K Root Mean Squared Error (RMSE)
%U http://www.cwejournal.org/vol8no2/sodium-adsorption-ratio-sar-prediction-of-the-chalghazi-river-using-artificial-neural-network-ann-iran/