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Prediction Models for the Estimation of Soil Moisture Content

[+] Author Affiliations
Swathi Gorthi, Huifang Dou

Utah State University, Logan, UT

Paper No. DETC2011-48259, pp. 945-953; 9 pages
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5480-8
  • Copyright © 2011 by ASME


This paper provides a survey on different kinds of prediction models developed for the estimation of soil moisture content of an area, using empirical information including meteorological and remotely sensed data. The different models employed extend over a wide range of machine learning techniques starting from Basic Linear Regression models through models based on Bayesian framework, Decision tree learning and Recursive partitioning, to the modern non-linear statistical data modeling tools like Artificial Neural Networks. The fundamental mathematical backgrounds, pros and cons, prediction results and efficiencies of all the models are discussed.

Copyright © 2011 by ASME
Topics: Soil



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