MENEZES, P. L.; http://lattes.cnpq.br/9781159016378038; MENEZES, Paulo Lopes de.
Resumo:
The efficiency of a sprinkler irrigation system depends on the
performance of the sprinkler head, which is dependent on certain characteristics such as
nozzle diameter, operating pressure, wind speed and direction, and layout and spacing
in the field. Determining the coefficients of uniformity and the efficiency of sprinkler
systems usually depends on field trials requiring time and financial resources. One
alternative to reduce time and expense is the use of simulations to estimate the values of
water distribution or the coefficients of uniformity and efficiency. In this study, we
proposed and tested the application of a multilayer perceptron-type artificial neural
network (ANN) to simulate the precipitation of a sprinkler having as input parameters
the values of operating pressure, wind speed, wind direction, and sprinkler nozzle
diameter. Trials were performed in the field with sprinklers operating in a grid of 16 x
16 collectors with 1.5 meter spacing, with different combinations of nozzles, pressures,
and wind conditions. An artificial neural network was trained to simulate and estimate
the water distribution values for the sprinkler at conditions tested. The ANN model
showed good results in the simulation of precipitation, with the Spearman correlation (
) between the data obtained in the field trial and the simulated data having values
between 0.92 and 0.97 for the ten trials analyzed. For the correlation between the
distribution profile with the data simulated by the ANN model and the data obtained in
field trials, for the same ten trials, R2 coefficient values of 0.95 to 0.99 were observed.
The ANN model shows promise in the simulation of water distribution of sprinklers in
sprinkler irrigation systems.