General descriptions of the study region and the studied soils.

General descriptions of the study region and the studied soils.</p

Developed PTFs for predicting <i>K</i>_<i>ψ</i> using easily measurable soil attributes by applying the SMLR method.

Developed PTFs for predicting Kψ using easily measurable soil attributes by applying the SMLR method.</p

Measurement of <i>K</i>_<i>ψ</i> by tension-disk infiltrometer at different land uses.

Measurement of Kψ by tension-disk infiltrometer at different land uses.</p

Different classifications for the selected performance criteria.

Different classifications for the selected performance criteria.</p

Pearson’s correlation coefficients (R) among studied soil attributes.

Pearson’s correlation coefficients (R) among studied soil attributes.</p

Scatter plots of the measured (observed) versus the predicted <i>K</i>₁₅, <i>K</i>₁₀, <i>K</i>₅, <i>K</i>₀ by applying MLPNNs approach as the best predictor and using easily measurable soil attributes.

Scatter plots of the measured (observed) versus the predicted K15, K10, K5, K0 by applying MLPNNs approach as the best predictor and using easily measurable soil attributes.</p

The descriptive statistics and related normality test parameters for the selected attributes of the studied soils (<i>n</i> = 102).

The descriptive statistics and related normality test parameters for the selected attributes of the studied soils (n = 102).</p

Highlights and Raw data are available as supplementary files.

Highlights and Raw data are available as supplementary files.</p

The performance criteria for predicting <i>K</i>_<i>ψ</i> using easily measurable soil attributes by applying the SMLR, MLPNNs, and RBFNNs approaches.

The performance criteria for predicting Kψ using easily measurable soil attributes by applying the SMLR, MLPNNs, and RBFNNs approaches.</p

The United State Department of Agriculture (USDA) soil textural classes (<i>n</i> = 102).

The United State Department of Agriculture (USDA) soil textural classes (n = 102).</p