Estimation of Furrow Irrigation Sediment Loss Using an Artificial Neural Network
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VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 1
Abstract
The area irrigated by furrow irrigation in the United States has been steadily decreasing but still represents about 20% of the total irrigated area in the United States. Furrow irrigation sediment loss is a major water quality issue, and a method for estimating sediment loss is needed to quantify the environmental effects and estimate effectiveness and economic value of conservation practices. Artificial neural network (NN) modeling was applied to furrow irrigation to predict sediment loss as a function of hydraulic and soil conditions. A data set consisting of 1,926 furrow evaluations, spanning three continents and a wide range of hydraulic and soil conditions, was used to train and test a multilayer perceptron feed forward NN model. The final NN model consisted of 16 inputs, 19 hidden nodes in a single hidden layer, and 1 output node. Model efficiency (ME) of the NN model was for the training data set and for the test data set. The prediction performance for the complete data set of 1,926 furrow evaluations was with an absolute sediment loss prediction error of less than , , , and per furrow for 35, 53, 72, and 85% of the data set values, respectively. The NN model is applicable to predicting sediment loss rates between 1 and 300 kg per furrow for furrow lengths between 30 and 400 m, slopes between 0.1 and 4%, flow rates between 5 and , and silt or sand particle–sized fractions between 0.1 and 0.75.
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Acknowledgments
The participation of L. Mateos was as part of the project CTM2013-45666-R (Spanish Program R+D+i).
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© 2015 American Society of Civil Engineers.
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Received: Feb 6, 2015
Accepted: May 20, 2015
Published online: Jul 8, 2015
Discussion open until: Dec 8, 2015
Published in print: Jan 1, 2016
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