%0 Journal Article
%T Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models
%A R. Rezaei Arshad
%A Gh. Sayyad
%A M. Mosaddeghi
%A B. Gharabaghi
%J ISRN Soil Science
%D 2013
%R 10.1155/2013/308159
%X 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 . 1. Introduction Soil hydraulic properties such as saturated hydraulic conductivity govern many soil hydrological processes; therefore, they are very important and even necessary in water and mass transport models and irrigation and drainage studies [1]. Direct measurement of soil hydraulic properties including is costly and time-consuming and becomes impractical due to spatial and temporal variabilities when hydrologic predictions are needed for large areas. Also it requires sophisticated measurement devices and skilled operators [2]. In the past few decades, as an alternative, indirect approximation of hydraulic properties from some basic and easily measured soil properties (such as clay, sand, and silt contents, and bulk density) using pedotransfer functions (PTFs) has received considerable acceptance [3¨C7]. ¡°Pedotransfer function¡± was first introduced for empirical regression equations relating water and solute transport parameters to the basic soil properties that are available in soil survey [8]. The is an important soil hydraulic property often estimated using PTFs. Different methods such as regression models [3, 9¨C11] and artificial neural networks (ANN) are available for derivation of PTFs. In recent years,
%U http://www.hindawi.com/journals/isrn.soil.science/2013/308159/