Background and Objective: Direct measurement of saturated hydraulic conductivity of soil is time consuming and costly and today this parameter can be estimated using soil retrieval parameters. Therefore, this study aimed to use regression modeling to estimate the satura
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Background and Objective: Direct measurement of saturated hydraulic conductivity of soil is time consuming and costly and today this parameter can be estimated using soil retrieval parameters. Therefore, this study aimed to use regression modeling to estimate the saturated hydraulic conductivity of soil based on grain size parameters i.e. d10, d50, and d60.
Material and Methodology: First, 25 soil samples with sandy texture were randomly collected in the spring of 2017 from the agricultural lands of Saqez city and the samples were collected in a container and taken to the laboratory for analysis and hydraulic guidance using the Darcy’s law was calculated. Using the available data, univariate and multivariate regression relationships were fitted on the data and based on the model evaluation statistics, the relationship that had the best estimate of saturated hydraulic conductivity of soil was determined.
Findings: The results of this study showed that the linear equation with 3 inputs saturated hydraulic conductivity of soil more accurately than the equations with 1 or 2 inputs. The results showed that the parameter d10 had a more effective role for estimating saturated hydraulic conductivity of soil than the parameters d50 and d60 and the effective parameter for comparison of saturated hydraulic conductivity is called d10.
Discussion and Conclusion: The main purpose of this study was to provide models that can estimate the saturated hydraulic conductivity of soil with cost reduction and time savings with acceptable precision, and in summary, regression modeling can be used to estimate the saturated hydraulic conductivity of soil.
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