Desenvolvimento de um modelo neural para estimar a massa de sólidos acumulada na filtração de água na irrigação localizada

Abstract

Water conservation is a matter of great importance and, for this reason, new techniques are constantly being developed to minimize this challenge. In Brazil, localized irrigation techniques have been widely adopted to maximize water use efficiency. However, the effectiveness of this technique is compromised by the clogging of the water emitters orifices by solid particles of organic and inorganic matter that accumulate in the filters themselves. That's why, Artificial Neural Networks were employed to estimate this mass of solids accumulated in filtration, aiming for the best conditions for it to be as low as possible, thereby minimally affecting the water flow rate. Using an experimental database available in the literature, a neural model was implemented using the Levenberg-Marquardt learning algorithm, proving to be extremely effective in this case, with an average percentage relative error of 0.29\% in predicting values of mass of solids accumulated.