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Hossein Bayat

Hossein Bayat

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 25221255600
HIndex:
Faculty: Faculty of Agriculture
Address: Associate Professor (Ph. D.), Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran.
Phone: 09188188378

Research

Title
PARAMETRIC ESTIMATION OF WATER RETENTION USING MGMDH METHOD AND PRINCIPAL COMPONENT ANALYSIS
Type
JournalPaper
Keywords
Multi-objective group method of data handling; pedotransfer function; principal component analysis; Parametric estimation
Year
2016
Journal Polish Journal of Soil Science
DOI
Researchers mohammad reza Neyshaburi ، Hossein Bayat ، ، Kourosh Mohammadi ، Andrew S. Gregory ، Nader Nariman-Zadeh

Abstract

Performing primary analysis, such as principal component analysis (PCA) may increase accuracy and reliability of developed pedotransfer functions (PTFs). This study focuses on the usefulness of soil penetration resistance (PR) and principal components (PCs) as new inputs along with the others to develop PTFs for estimating soil water retention curve (SWRC) using multi-objective group method of data handling (mGMDH). The Brooks and Corey (1964) SWRC model was used to give a description of the water retention curves and its parameters were determined from experimental SWRC data. To select eight PCs, PCA was applied to all measured or calculated variables. Penetration resistance, organic matter (OM), aggregates mean weight diameter (MWD), saturated hydraulic conductivity (Ks), macro porosity (Mp), micro porosity (Mip) and eight selected PCs were used as predictors to estimate Brooks and Corey model parameters by mGMDH. Using PR or OM, Ks and MWD, improved the estimation of SWRC in some cases. Using predicted PR can be useful in the estimation of SWRC. Using either the MP and Mip or the eight PCs significantly improved the PTFs accuracy and reliability. It would be very useful to apply PCA on the original variables as a primary analysis to develop parametric PTFs.