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.
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