• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
Desert
Articles in Press
Current Issue
Journal Archive
Volume Volume 24 (2019)
Volume Volume 23 (2018)
Volume Volume 22 (2017)
Volume Volume 21 (2016)
Volume Volume 20 (2015)
Volume Volume 19 (2014)
Volume Volume 18 (2013)
Volume Volume 17 (2012)
Volume Volume 16 (2011)
Volume Volume 15 (2010)
Issue Issue 2
Summer and Autumn 2010, Page 71-167
Issue Issue 1
Winter and Spring 2010, Page 1-69
Volume Volume 14 (2009)
Volume Volume 13 (2008)
Volume Volume 12 (2007)
Volume Volume 11 (2006)
Volume Volume 10 (2005)
Keshavarzi, A., Sarmadian, F. (2010). Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region). Desert, 15(2), 167-174. doi: 10.22059/jdesert.2011.23014
A Keshavarzi; F Sarmadian. "Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)". Desert, 15, 2, 2010, 167-174. doi: 10.22059/jdesert.2011.23014
Keshavarzi, A., Sarmadian, F. (2010). 'Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)', Desert, 15(2), pp. 167-174. doi: 10.22059/jdesert.2011.23014
Keshavarzi, A., Sarmadian, F. Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region). Desert, 2010; 15(2): 167-174. doi: 10.22059/jdesert.2011.23014

Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)

Article 12, Volume 15, Issue 2, Summer and Autumn 2010, Page 167-174  XML PDF (124.01 K)
DOI: 10.22059/jdesert.2011.23014
Authors
A Keshavarzi; F Sarmadian
Faculty of Soil and Water Engineering, University of Tehran, Iran
Abstract
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, multivariate regression and neural network model (feed-forward back propagation network) were employed to develop a pedotransfer function for predicting soil parameter using easily measurable characteristics of clay and organic carbon. The performance of the multivariate regression and neural network model was evaluated using a test data set. In order to evaluate the models, root mean square error (RMSE) was used. The value of RMSE and R2 derived by ANN model for CEC were 0.47 and 0.94 respectively, while these parameters for multivariate regression model were 0.65 and 0.88 respectively. Results showed that artificial neural network with seven neurons in hidden layer had better performance in predicting soil cation exchange capacity than multivariate regression.
Keywords
CEC; easily measurable characteristics; Feed-forward back propagation; hidden layer; Pedotransfer functions; Ziaran
Statistics
Article View: 2,572
PDF Download: 2,949
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Journal Management System. Designed by sinaweb.