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Arabian Journal of Geosciences

, 11:566 | Cite as

Predicting moisture content of soil from thermal properties using artificial neural network

  • Oluseun Adetola Sanuade
  • Peter Adetokunbo
  • Michael Adeyinka Oladunjoye
  • Abayomi Adesola Olaojo
Original Paper
  • 72 Downloads

Abstract

Monitoring of soil moisture contents is an important practice for irrigation water management. The benefit of periodic soil water content data includes improved irrigation scheduling in order to optimize water usage for improved crop productivity. However, the in situ equipment for measuring soil water contents have high maintenance and operation cost and are highly affected by neighboring soil conditions, and some have overwhelming calibration and data interpretation, whereas the common standard laboratory procedure requires much effort and can be time-consuming for large dataset. The objective of this study is to evaluate the applicability of artificial neural network (ANN) to predict moisture content of soil using available or measured thermal properties (thermal conductivity, thermal diffusivity, specific heat, and temperature) of soil. We used both multilayered perception (MLP) and radial basis function (RBF) types of ANN. The study area is a farmland situated within the premises of the University of Ibadan campus. Thermal properties were measured with KD2 Pro at 42 points along seven transects. Soil samples were also collected at these points to determine their moisture contents in the laboratory. ANN analysis carried out effectively predicted the soil moisture content with very low root-mean-square error (RMSE) and high correlation coefficient (R) of approximately 0.9 for the two methods evaluated. The overall results suggest that ANN can be incorporated to predict the moisture content of soil in this area where thermal properties are known.

Keywords

Thermal properties Moisture content Artificial neural network 

Notes

Acknowledgments

The authors acknowledge the staff of the Faculty of Agriculture, University of Ibadan, for the permission to carry out the research in their agricultural farmland. We also thank King Fahd University of Petroleum & Minerals for providing MATLAB and other resources for this work.

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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.Geosciences DepartmentKing Fahd University of Petroleum & MineralsDhahranSaudi Arabia
  2. 2.Department of GeophysicsFederal University Oye-EkitiOyeNigeria
  3. 3.Department of GeologyState University of New York at BuffaloNew YorkUSA
  4. 4.Department of GeologyUniversity of IbadanIbadanNigeria
  5. 5.Department of Earth SciencesAjayi Crowther UniversityOyoNigeria

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