Volume 16, Issue 60 (Summer 2012)                   JWSS 2012, 16(60): 107-118 | Back to browse issues page


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R. Rezae Arshad, GH. Sayyad, *, M. Mazloom, M. Shorafa, A. Jafarnejady. Comparison of Artificial Neural Networks and Regression Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity in Soils of Khuzestan Province. JWSS. 2012; 16 (60) :107-118
URL: http://jstnar.iut.ac.ir/article-1-2310-en.html

Abstract:   (14048 Views)
Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.
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Type of Study: Research | Subject: Ggeneral
Received: 2012/09/11

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