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Estimation of furrow irrigation sediment loss using an artificial neural network

Bradley A. King, David L. Bjorneberg, Thomas J. Trout, Luciano Mateos, Danielle F. Araujo, Raimundo N. Costa
Journal of Irrigation and Drainage Engineering 2015 pp. 04015031
conservation practices, data collection, economic valuation, environmental impact, furrow irrigation, furrows, irrigated farming, neural networks, prediction, sand, sediment yield, silt, soil, water quality
The area irrigated by furrow irrigation in the U.S. has been steadily decreasing but still represents about 20% of the total irrigated area in the U.S. Furrow irrigation sediment loss is a major water quality issue and a method for estimating sediment loss is needed to quantify the environmental impacts 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 1926 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. Prediction performance of the NN model was model efficiency (ME) = 0.66 for the training data set and ME = 0.80 for the testing data set. The prediction performance for the complete data set of 1926 furrow evaluations was ME= 0.70 with an absolute sediment loss prediction error of less than ±5, ±10, ±20, and ±30 kg 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 m and 400 m, slopes between 0.1% and 4%, flow rates between 5 L/min-1 and 75 L/min-1, and silt or sand particle sized fractions between 0.1 and 0.75.