Evaluating the efficiency of artificial neural network in prediction of Electrical conductivity of Zarrinehroud River

Subject Areas : Water and Environment

Ali Khoshnazar ¹ (Professor of University, Environmental Faculty, Tehran, Iran.)
Touraj Nasrabadi ² (Assistant Professor, Environmental faculty, Tehran University, Iran.)
Pouyan Abbasi Maedeh ³ (MSc Student, International Aras Campus , Tehran University, Iran.)

Received: 2012-04-25 Accepted : 2012-09-07 Published : 2012-09-22

Keywords: Zarrinehroud, Electro conductivity, Neural Network, Prediction,

Abstract :

Sixteen stations on Zarrinehroud River were sampled and parameters like temperature, alkalinity, Ph, electrical conductivity, dissolved oxygen and major anions and cations were measured on water samples. Afterwards, Pearson correlation coefficient between EC and other parameters were determined and the ones with lower cost of measurement were considered as the inputs of neural network models. Finally, the model number 5 with tangent Simulating algorithm and Levenberg-Marquet training Algorithm with minimum prediction error was accepted. The maximum determination coefficient, RMSE and NRMSE Were estimated to be 0.98, 168.33 and 0.28 respectively. Furthermore, it is observed that pH has a remarkable sensitivity more over 60 percent on the artificial neural network prediction.

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