The study aims to indirectly determine the saturated hydraulic conductivity (Ks). The applicability of recently-published pedotransfer functions (PTFs) based on a machine learning approach has been tested, and their performance has been compared with well-known hierarchical PTFs (computer software Rosetta) for 126 soil data sets in the Czech Republic. The quality of estimates has been statistically evaluated in comparison with the measured Ks values; the root mean squared error (RMSE), the mean error (ME) and the coefficient of determination (R2) were considered. The eight tested models of PTFs were ranked according to the RMSE values. The measured results reflected high Ks variability between and within the study areas, especially for those areas where preferential flow occurred. In most cases, the tested PTFs overestimated the measured Ks values, which is documented by positive ME values. The RMSE values of the Ks estimate ranged on average from 0.5 (coarse-textured soils) to 1.3 (medium to fine-textured soils) for log-transformed Ks in cm/day. Generally, the models based on Random Forest performed better than those based on Boosted Regression Trees. However, the best estimates were obtained by Neural Network analysis PTFs in Rosetta, which scored for four best rankings out of five.
The determination of the saturated hydraulic conductivity Ks on a field scale presents a challenge in which several variables have to be considered. As there is no benchmark or reference method for the Ks determination, the suitability of each available method has to be evaluated. This study is aimed at the functional evaluation of three publicly available types of pedotransfer functions (PTFs) with different levels of utilised predictors. In total, ten PTF models were applied to the 56 data sets including the measured Ks value and the required predictors (% sand, silt and clay particles, dry bulk density, and organic matter/organic carbon content). A single agricultural field with a relatively homogenous particle size distribution was selected for the study to evaluate the ability of the PTF to reflect the variability of Ks. The correlation coefficient, coefficient of determination, mean error, and root mean square error were determined to evaluate the Ks prediction quality. The results showed a high variability in Ks within the field; the measured Ks values ranged between 10 and 1261 cm/day. Although the tested PTF models are based on a robust background of soil databases, they could not provide estimates with satisfactory accuracy unless local soil data were incorporated into the PTF development.
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