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

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

Materials and Geoenvironment
Volume 64 (2017): Issue 3 (September 2017)
By: Oluseun Adetola Sanuade,  Rasheed Babatunde Adesina,  Joel Olayide Amosun,  Akindeji Opeyemi Fajana and  Olayiwola Grace Olaseeni  
Open Access
|Dec 2017

Authors

Oluseun Adetola Sanuade

sheunsky@gmail.com

Department of Geophysics, Federal University Oye-Ekiti,, Ekiti, Nigeria

Rasheed Babatunde Adesina

Department of Marine Science and Technology, Federal University of Technology,, Akure, Nigeria

Joel Olayide Amosun

Department of Geophysics, Federal University Oye-Ekiti,, Ekiti State, Nigeria

Akindeji Opeyemi Fajana

Department of Geophysics, Federal University Oye-Ekiti,, Ekiti State, Nigeria

Olayiwola Grace Olaseeni

Department of Geophysics, Federal University Oye-Ekiti,, Ekiti State, Nigeria
DOI: https://doi.org/10.1515/rmzmag-2017-0012 | Journal eISSN: 1854-7400
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Language: English
Page range: 169 - 180
Submitted on: Apr 20, 2017
|
Accepted on: May 25, 2017
|
Published on: Dec 29, 2017
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
thermal conductivity,
ANN,
MATLAB,
prediction,
model
Related subjects:
Computer sciences,
Databases and data mining,
Geosciences,
Geodesy,
Geology and mineralogy,
Materials sciences,
Materials characterization and properties

© 2017 Oluseun Adetola Sanuade, Rasheed Babatunde Adesina, Joel Olayide Amosun, Akindeji Opeyemi Fajana, Olayiwola Grace Olaseeni, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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