  Soil water retention curve (SWRC) and saturated hydraulic conductivity (SHC)
are key hydraulic properties for unsaturated zone hydrology and groundwater. In
particular, SWRC provides useful information on entry pore-size distribution,
and SHC is required for flow and transport modeling in the hydrologic cycle.
Not only the SWRC and SHC measurements are time-consuming, but also scale
dependent. This means as soil column volume increases, variability of the SWRC
and SHC decreases. Although prediction of the SWRC and SHC from available
parameters, such as textural data, organic matter, and bulk density have been
under investigation for decades, up to now no research has focused on the
effect of measurement scale on the soil hydraulic properties pedotransfer
functions development. In the literature, several data mining approaches have
been applied, such as multiple linear regression, artificial neural networks,
group method of data handling. However, in this study we develop pedotransfer
functions using a novel approach called contrast pattern aided regression
(CPXR) and compare it with the multiple linear regression method. For this
purpose, two databases including 210 and 213 soil samples are collected to
develop and evaluate pedotransfer functions for the SWRC and SHC, respectively,
from the UNSODA database. The 10-fold cross-validation method is applied to
evaluate the accuracy and reliability of the proposed regression-based models.
Our results show that including measurement scale parameters, such as sample
internal diameter and length could substantially improve the accuracy of the
SWRC and SHC pedotransfer functions developed using the CPXR method, while this
is not the case when MLR is used. Moreover, the CPXR method yields remarkably
more accurate soil water retention curve and saturated hydraulic conductivity
predictions than the MLR approach.
