Application of Artificial Neural Network to Predict TDS in Talkheh Rud River
Publication: Journal of Irrigation and Drainage Engineering
Volume 138, Issue 4
Abstract
Salinity ranks high among the list of parameters which demand attention during the planning and management of water quality, particularly for drinking and irrigation. If water quality is adequately predicted, then the proper management is possible within the time. Looking into this importance, in the present study, an artificial neural network (ANN) model was developed to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. Two ANN networks viz, multilayer perceptron (MLP) and recurrent neural network (RNN), which are further referred as the Elman network were developed and applied to the Talkheh Rud River. Comparing the results of the TDS at two monitoring stations, it was observed that the Elman network predicts the TDS very close to the observed values (R = 0.9639). Possession of 1 month’s worth of TDS data beforehand may be helpful for the water quality management decision-making process.
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Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 138 • Issue 4 • April 2012
Pages: 363 - 370
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Oct 20, 2010
Accepted: Jul 12, 2011
Published online: Oct 20, 2011
Published in print: Apr 1, 2012
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