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Scientia Iranica
Article . 2017 . Peer-reviewed
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Scientia Iranica
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The Estimation of van Genuchten SWCC Model for Unsaturated Sands by means of the Genetic Programming

Authors: A. Taban; M . Mirmohammad Sadeghi; M.A. Rowshanzamir;

The Estimation of van Genuchten SWCC Model for Unsaturated Sands by means of the Genetic Programming

Abstract

The van Genuchten Model (1980) is widely-used for the description of the Soil-Water Characteristic Curve (SWCC) of a variety of soils. This study uses the Genetic Programming (GP) for the presentation of equations estimating the van Genuchten (vG) Model fitting parameters for unsaturated clean sand soils. Moreover, this study uses the data derived from the valid dataset of Benson et al. (2014), including 95 measured SWCCs in both drying and wetting phases. The data on the particle size distributions includes the fine-grain percentage (Fines %), d60, d10, besides the residual and saturated volumetric water content and ), as the GP model inputs of set of terminal. As for the model outputs of set of terminal, the fitting parameters for the vG model include a and n. The functions used in the GP training were 'plus', 'minus', 'times', taken from the MATLAB default functions, 'mydivide' proposed by Silva (2007), and some other new power functions included by this study. Accordingly, new equations were presented for the estimation of vG Model fitting parameters for both forms of wetting and drying. Finally, to evaluate the accuracy of the proposed estimation equations, the GP results were evaluated and verified in different procedures.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
gold