Using artificial neural network to predict dry density of soil from thermal conductivity
| Autor: | Olayiwola G. Olaseeni, Joel Olayide Amosun, Oluseun Adetola Sanuade, Rasheed Babatunde Adesina, Akindeji Opeyemi Fajana |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
QE1-996.5
model Mining engineering. Metallurgy Artificial neural network 020209 energy TN1-997 Soil science Geology 02 engineering and technology prediction Thermal conductivity matlab 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering thermal conductivity 0204 chemical engineering MATLAB Dry density computer ann computer.programming_language |
| Zdroj: | Materials and Geoenvironment, Vol 64, Iss 3, Pp 169-180 (2017) |
| ISSN: | 1854-7400 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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