%0 Journal Article %1 WOS:000366751400009 %A King, Bradley A %A Bjorneberg, David L %A Trout, Thomas J %A Mateos, Luciano %A Araujo, Danielle F %A Costa, Raimundo N %C 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA %D 2016 %I ASCE-AMER SOC CIVIL ENGINEERS %J JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING %K imported %N 1 %R 10.1061/(ASCE)IR.1943-4774.0000932 %T Estimation of Furrow Irrigation Sediment Loss Using an Artificial Neural Network %V 142 %X 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 ME=0.66 for the training data set and ME=0.80 for the test data set. The prediction performance for the complete data set of 1,926 furrow evaluations was ME=0.70 with an absolute sediment loss prediction error of less than +/- 5, +/- 10, +/- 20, and +/- 30kg 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 300kg per furrow for furrow lengths between 30 and 400m, slopes between 0.1 and 4%, flow rates between 5 and 75Lmin-1, and silt or sand particle-sized fractions between 0.1 and 0.75. (C) 2015 American Society of Civil Engineers.

@article{WOS:000366751400009, 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 ME=0.66 for the training data set and ME=0.80 for the test data set. The prediction performance for the complete data set of 1,926 furrow evaluations was ME=0.70 with an absolute sediment loss prediction error of less than +/- 5, +/- 10, +/- 20, and +/- 30kg 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 300kg per furrow for furrow lengths between 30 and 400m, slopes between 0.1 and 4%, flow rates between 5 and 75Lmin-1, and silt or sand particle-sized fractions between 0.1 and 0.75. (C) 2015 American Society of Civil Engineers.}, added-at = {2022-05-23T20:00:14.000+0200}, address = {1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA}, author = {King, Bradley A and Bjorneberg, David L and Trout, Thomas J and Mateos, Luciano and Araujo, Danielle F and Costa, Raimundo N}, biburl = {https://www.bibsonomy.org/bibtex/28940c5c5625b476da3c3aeeeba483b7d/ppgfis_ufc_br}, doi = {10.1061/(ASCE)IR.1943-4774.0000932}, interhash = {a35ce38e30701256106ecc9ece3cfd49}, intrahash = {8940c5c5625b476da3c3aeeeba483b7d}, issn = {0733-9437}, journal = {JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING}, keywords = {imported}, number = 1, publisher = {ASCE-AMER SOC CIVIL ENGINEERS}, pubstate = {published}, timestamp = {2022-05-23T20:00:14.000+0200}, title = {Estimation of Furrow Irrigation Sediment Loss Using an Artificial Neural Network}, tppubtype = {article}, volume = 142, year = 2016 }