Resumen

COBA, Darina Lara; HERRERA SUAREZ, Miguel; GARCIA LORENZO, María Matilde  y  BELTRAN, Roberto. Computational Model to Predict Soil Density Using Machine Learning Methods. Rev Cie Téc Agr [online]. 2018, vol.27, n.1, pp.46-53. ISSN 2071-0054.

The machine learning methods have been used successfully in the calculation of parameters of various problems of engineering, in which the complicated variables have a relation nonlinear among themselves and the modelation does not enable representing the intervening problem through a mathematical function of easy deduction. For the estimation of soil properties several variables are analyzed that make their estimation by means of mathematical models is a complex process transferring the problem solution to artificial intelligence field. The present work aims at developing a mathematical model for the estimation of soil density through the on-the-go soil sensing, a method of automatized learning. The computational learning automated tool used was WEKA, by means of which three procedures of automatized learning applied (multilayer perceptron neuronal artificial nets and K-nearest neighbor). The validation of the model came true by means of the crossed and experimental validation. Results evidence that the best method is the K-nearest neighbor with absolute mean error of 0.06 and a correlation coefficient of 0.89; variables of bigger weight in prediction were moisture content followed by work speed, power, width of the working tool and the depth.

Palabras clave : Bulk density; artificial intelligence; soil compaction; prediction of soil compaction.

        · resumen en Español     · texto en Español     · Español ( pdf )