Artificial neural network based equation to estimate head loss along drip irrigation laterals

Abstract

This work proposes an equation based on Artificial Neural Network (ANN) to estimate head loss along emitting pipes accounting for cylindrical in-line emitters. The following input variables were used to fit the model: total head loss between two consecutive emitters; emitter spacing; internal diameter of the pipe; mean water velocity at uniform pipe sections; and, kinematic viscosity of water. The input data was obtained by experimental means and standardized from 0 to 1. Five replications and six distinct structures of ANNs multilayer perceptron (MLP) were used during the training stage performed using the package neuralnet of the software R. A MLP structure consisting of six neurons at input layer, six neurons at hidden layer, and one neuron at output layer was applied for fitting the model. Estimated values by the ANN’s equation were compared to the estimated values by an equation based on dimensional analysis. The ANN’s equation and the equation based on dimensional analysis presented maximum deviations between measured and estimated values of 0.324 kPa and 1.647 kPa, respectively. Therefore the ANN’s equation presented better results than the equation based on dimensional analysis.