Table of Contents
ISRN Soil Science
Volume 2013, Article ID 308159, 8 pages
http://dx.doi.org/10.1155/2013/308159
Research Article

Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models

1Department of Soil Science, Faculty of Agrriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
3School of Engineering, University of Guelph, Guelph, ON, Canada

Received 7 April 2013; Accepted 12 May 2013

Academic Editors: G. Benckiser, D. Hui, H. K. Pant, and D. Zhou

Copyright © 2013 R. Rezaei Arshad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Saturated hydraulic conductivity ( ), among other soil hydraulic properties, is important and necessary in water and mass transport models and irrigation and drainage studies. Although this property can be measured directly, its measurement is difficult and very variable in space and time. Thus pedotransfer functions (PTFs) provide an alternative way to predict the from easily available soil data. This study was done to predict the in Khuzestan province, southwest Iran. Three Intelligence models including (radial basis function neural networks (RBFNN), multi layer perceptron neural networks (MLPNN)), adaptive neuro-fuzzy inference system (ANFIS) and multiple-linear regression (MLR) to predict the were used. Input variable included sand, silt, and clay percents and bulk density. The total of 175 soil samples was divided into two groups as 130 for the training and 45 for the testing of PTFs. The results indicated that ANFIS and RBFNN are effective methods for prediction and have better accuracy compared with the MLPNN and MLR models. The correlation between predicted and measured values using ANFIS was better than artificial neural network (ANN). Mean square error values for ANFIS, ANN, and MLR were 0.005, 0.02, and 0.17, respectively, which shows that ANFIS model is a powerful tool and has better performance than ANN and MLR in prediction of .