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Position: Home > Articles > Estimating Water Retention with Pedotransfer Functions Using Multi-Objective Group Method of Data Handling and ANNs Pedosphere 2011,21 (1)

Estimating Water Retention with Pedotransfer Functions Using Multi-Objective Group Method of Data Handling and ANNs

作  者：
Hossein Bayat;M R Neyshabouri;Kourosh Mohammadi;N. Nariman-Zade
单  位：
Department of Soil Science, Faculty of Agriculture, Tabriz University, Tabriz (Iran);Department of Irrigation and Drainage Engineering, Tarbiat Modares University, Tehran (Iran);Department of Mechanical Engineering, Engineering Faculty, Guilan University, Rasht (Iran;Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamadan (Iran)
关键词：
ptfs;estimate;psdfps;asdfps;parametric;input
摘  要：
Pedotransfer functions (PTFs) have been developed to estimate soil water retention curves (SWRC) by various techniques. In this study PTFs were developed to estimate the parameters (theta(s), theta(r), alpha and lambda) of the Brooks and Corey model from a data set of 148 samples. Particle and aggregate size distribution fractal parameters (PSDFPs and ASDFPs, respectively) were computed from three fractal models for either particle or aggregate size distribution. The most effective model in each group was determined by sensitivity analysis. Along with the other variables, the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling (mGMDH) and different topologies of artificial neural networks (ANNs). The architecture of ANNs for parametric PTFs was different regarding the type of ANN, output layer transfer functions and the number of hidden neurons. Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs. The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of theta(s). The textural fraction variables in PTF1 for the estimation of alpha were replaced with PSDFPs in PTF3. The use of ASDFPs as inputs significantly improved alpha estimates in the model. This result highlights the importance of ASDFPs in developing parametric PTFs. The mGMDH technique performed significantly better than ANNs in most PTFs.

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