Volume 15, Issue 55 (spring 2011)                   JWSS 2011, 15(55): 169-182 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

S. Moallemi, N.Davatgar. Comparison of Artificial Neural Network and Regression Pedotransfer Functions for Prediction of Cation Exchange Capacity in Guilan Province Soils. JWSS. 2011; 15 (55) :169-182
URL: http://jstnar.iut.ac.ir/article-1-1555-en.html
Abstract:   (20203 Views)
Measuring the cation exchange capacity (CEC) as one of the most important chemical soil properties is very time consuming and costly. Pedotransfer functions (PTFs) provide an alternative to direct measurement by estimating CEC. The objective of this study was to develop PTFs for predicting CEC of Guilan province soils using artificial neural network (ANN) and multiple-linear regression method and also determine whether grouping based on soil textural class and organic carbon content improved estimating CEC by two methods. For this study, 1662 soil samples of Guilan province were used from soil chemistry laboratory database of Rice Research Institute. 1109 data were used for training (the development of PTFs) and 553 data for testing (the validation of PTFs) of the models. The results showed that organic carbon was the most important variable in the estimation of cation exchange capacity for total data and all classes in textural and organic C groups in both methods. ANN performed better than the regression method in predicting CEC in all data, and grouping of data only improved the prediction of PTFs in Sand and Sandy clay loam classes by ANN method.
Full-Text [PDF 6018 kb]   (2113 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2011/06/26

Add your comments about this article : Your username or Email:
Write the security code in the box

© 2015 All Rights Reserved | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb

تحت نظارت وف ایرانی آسپا-وف