| Abstrakt: |
In the most recent literature, predictive models for the hydraulic conductivity (K) estimation of sandy soils based on particle size distribution (PSD) have received harsh criticism for their inaccurate predictions outside the original study area. This study examines the best possible relationships between K and complete PSD characteristics, porosity (n), and field dry density (${\rho _d}$ ρ d ) of the 60 different samples from river deposition settings. In comparison to other grain sizes, the goodness of the nonlinear fit of ${\bf{\it{K}}} - {d_{50}}$ K − d 50 (grain size at 50% passing) relationship was found to be the highest. Uniformity coefficient (Cu) and coefficient of curvature (Cc) were found to be the poor estimators of K. Whereas, ${\rho _d}$ ρ d and n showed significant nonlinear negative relations with the K. After integrating ${d_{50}}$ d 50 , n, and ${\rho _d}$ ρ d into a multivariate system and using nonlinear regression the strength of the relationship highly improved resulting in a novel equation for the K estimation. The values of statistical indicators such as R2, Adjusted R2, SSE, RMSE, and MSE i.e. 0.78, 0.77, 25.89, 0.67, and 0.45 respectively, reveal the adequacy and significant performance of the developed regression model. Also, the proposed equation, validated on an independent dataset of 21 samples, showed exceptional prediction accuracy. [ABSTRACT FROM AUTHOR] |
