What is it about?
Artificial neural network was used to predict the dry density of soil from its thermal conductivity using MATLAB 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.
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Why is it important?
The model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.
Perspectives
I hope this article will help readers to understand the use of ANN for prediction of parameter from another parameter. Most of the real life situations are non-linear and ANN can be used to solve the problems of non-linearity between parameters. This article will readers to understand the principle of ANN
Mr Oluseun Adetola Sanuade
Oklahoma State University System
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This page is a summary of: Using artificial neural network to predict dry density of soil from thermal conductivity, Materials and Geoenvironment, September 2017, De Gruyter,
DOI: 10.1515/rmzmag-2017-0012.
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