<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (<italic>R</italic><sup>2</sup>) were calculated at 2.54% and 0.979, respectively.</p></abstract>

The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (R2) were calculated at 2.54% and 0.979, respectively.

Application of Computational Intelligence Methods for Predicting Soil Strength

Acta Technologica Agriculturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Application of Computational Intelligence Methods for Predicting Soil Strength"}

The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a...

Application of Computational Intelligence Methods for Predicting Soil Strength

Publicado en línea: 08 ago 2019

Páginas: 80 - 85

DOI: https://doi.org/10.2478/ata-2019-0015

Palabras clave
artificial neural network, ANFIS, cone index, modeling, Levenberg-Marquardt algorithm

© 2019 Yousef Abbaspour-Gilandeh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Application of Computational Intelligence Methods for Predicting Soil Strength

Publicado en línea: 08 ago 2019

Páginas: 80 - 85

DOI: https://doi.org/10.2478/ata-2019-0015

Palabras claveartificial neural network, ANFIS, cone index, modeling, Levenberg-Marquardt algorithm

© 2019 Yousef Abbaspour-Gilandeh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
artificial neural network, ANFIS, cone index, modeling, Levenberg-Marquardt algorithm