<abstract xmlns="http://www.w3.org/1999/xhtml"> Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.</abstract>

Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.

Using artificial neural network to predict dry density of soil from thermal conductivity

Materials and Geoenvironment

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

Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta,...

Using artificial neural network to predict dry density of soil from thermal conductivity

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 169 - 180

Eingereicht: 20. Apr. 2017

Akzeptiert: 25. Mai 2017

DOI: https://doi.org/10.1515/rmzmag-2017-0012

Schlüsselwörter
thermal conductivity, ANN, MATLAB, prediction, model

© 2018

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

Using artificial neural network to predict dry density of soil from thermal conductivity

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 169 - 180

Eingereicht: 20. Apr. 2017

Akzeptiert: 25. Mai 2017

DOI: https://doi.org/10.1515/rmzmag-2017-0012

Schlüsselwörterthermal conductivity, ANN, MATLAB, prediction, model

© 2018

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

Schlüsselwörter
thermal conductivity, ANN, MATLAB, prediction, model