Computational Model to Predict Soil Density Using Machine Learning Methods

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Published: Mar 19, 2018
Keywords:
Bulk density, artificial intelligence, soil compaction, prediction of soil compaction

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Darina Lara Coba
Universidad Tecnológica de la Habana José Antonio Echeverría
Miguel Herrera Suárez
Universidad Técnica de Manabí (UTM), Facultad de Ciencias Matemáticas Físicas y Químicas, Escuela de Mecánica
María Matilde García Lorenzo
Universidad Central “Marta Abreu” de Las Villas, Grupo de Inteligencia Artificial, Centro de Investigaciones Informáticas
Roberto Beltran
Universidad de las Fuerzas Armadas, Dpto. Energía y Mecánica

Abstract

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.

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How to Cite
Lara Coba, D., Herrera Suárez, M., García Lorenzo, M. M., & Beltran, R. (2018). Computational Model to Predict Soil Density Using Machine Learning Methods. Revista Ciencias Técnicas Agropecuarias, 27(1), 46–53. Retrieved from https://revistas.unah.edu.cu/index.php/rcta/article/view/859
Issue
Vol. 27 No. 1 (2018): January-February-March
Section
Original Articles

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Author Biographies

Darina Lara Coba, Universidad Tecnológica de la Habana José Antonio Echeverría

Dra.C., Prof. Auxiliar, Facultad de Ingeniería Mecánica, Departamento de Mecánica Aplicada

Miguel Herrera Suárez, Universidad Técnica de Manabí (UTM), Facultad de Ciencias Matemáticas Físicas y Químicas, Escuela de Mecánica

Dr.C

María Matilde García Lorenzo, Universidad Central “Marta Abreu” de Las Villas, Grupo de Inteligencia Artificial, Centro de Investigaciones Informáticas

Dra.C.

Roberto Beltran, Universidad de las Fuerzas Armadas, Dpto. Energía y Mecánica

M.Sc.

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