selection of a suitable model for the prediction of soil water content in north of iran

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ID: 246760
2015
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Abstract
Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Rosetta model were employed to develop pedotransfers functions (PTFs) for soil moisture prediction using available soil properties for northern soils of Iran. The Rosetta model is based on ANN works in a hierarchical approach to predict water retention curves. For this purpose, 240 soil samples were selected from the south of Guilan province, Gilevan region, northern Iran. The data set was divided into two subsets for calibration and testing of the models. The general performance of PTFs was evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean biased error between the observed and predicted values. Results showed that ANN with two hidden layers, Tan-sigmoid and linear functions for hidden and output layers respectively, performed better than the others in predicting soil moisture. In the other hand, ANN can model non-linear functions and showed to perform better than MLR. After ANN, MLR had better accuracy than Rosetta. The developed PTFs resulted in more accurate estimation at matric potentials of 100, 300, 500, 1000, 1500 kPa. Whereas, Rosetta model resulted in slightly better estimation than derived PTFs at matric potentials of 33 kPa. This research can provide the scientific basis for the study of soil hydraulic properties and be helpful for the estimation of soil water retention in other places with similar conditions, too.
Reference Key
esmaeelnejad2015spanishselection Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Leila Esmaeelnejad;Hassan Ramezanpour;Javad Seyedmohammadi;Mahmood Shabanpour
Journal Canadian Medical Association Journal
Year 2015
DOI
10.5424/sjar/2015131-6111
URL
http://revistas.inia.es/index.php/sjar/article/view/6111/2242
Keywords
neural networks multiple linear regression soil moisture curveagriculture

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