Abstract
ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs) ranging from 0.92 to 0.97 and Willmott agreement index (d) from 0.950 to 0.991, between the observed and simulated values for ten analysed trials. The ANN model shows promise in the simulation of precipitation in sprinkle irrigation systems.
Highlights
This study proposed the development of a multilayer perceptron (MLP) neural network model for the simulation of the precipitation of a sprinkler
The artificial neural network (ANN) with the best results was composed of one input layer with 4 neurons, one hidden layer with 280 neurons and the output layer with 256 neurons
The fact that the ANN with the best results has only one hidden layer agrees with the results observed by Soares et al (2014), who estimated soil water retention using ANN, and Zanetti et al (2008), who estimated reference evapotranspiration using ANNs and concluded that only one hidden layer is sufficient to represent the non-linear relationship between climatic elements and reference evapotranspiration
Summary
In order to ensure and improve crop yield, the technique of irrigation has been used as a complement or a substitute of natural rainfall, so as to guarantee that the water demand is met.Among the existing irrigation systems, sprinkle irrigation is one of the most used, since it can adapt to the diversities of soil, topography, crop and the size of the area to be cultivated, which allows a good control of the applied water depth and of its efficiency.In order to reduce costs and the waste of water and time in the evaluation of sprinkle irrigation systems, simulations can be used to predict the behavior and the results of irrigation.Many researchers have studied the importance of simulations, such as Faria et al (2012), who evaluated the applicability of a semiempirical model of Richards & Weatherhead for trials under different wind conditions, and Oliveira et al (2009), who evaluated the hypothesis of the existence of a linear relationship between the radius of throw of a gun type sprinkler and wind speed.In the context of the simulations, it is possible to use artificial neural networks (ANNs), which are computational models inspired in the neural structure of intelligent organisms, neurons and synapses, that acquire knowledge through experience. Among the existing irrigation systems, sprinkle irrigation is one of the most used, since it can adapt to the diversities of soil, topography, crop and the size of the area to be cultivated, which allows a good control of the applied water depth and of its efficiency. In order to reduce costs and the waste of water and time in the evaluation of sprinkle irrigation systems, simulations can be used to predict the behavior and the results of irrigation. In the context of the simulations, it is possible to use artificial neural networks (ANNs), which are computational models inspired in the neural structure of intelligent organisms, neurons and synapses, that acquire knowledge through experience. The most important property of ANNs is the ability to learn through examples, by adjusting the weights of the connections between neurons
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