application of artificial neural networks in prediction of saturated hydraulic conductivity using soil physical parameters

using artificial neural networks to estimate saturated hydraulic conductivity from easily available soil properties

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Prediction of Gas Lift Parameters Using Artificial Neural Networks

Estimating the Saturated Hydraulic Conductivity of Granular Material, Using Artificial Neural Network, Based on Grain Size Distribution Curve

Study of Saturated Hydraulic Conductivity Variations in Different Aggregate Size Distributions in an Agricultural Soil

Saturated hydraulic conductivity (Ks) is one of the most important soil physical characteristics that plays a major role in the soil hydrological behaviour. It is mainly affected by the soil structure characteristics. Aggregate size distribution is a measure of soil structure formation that can affect Ks. In this study, variations of Ks were investigated in various aggregate size distributions ...

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not req...

Changes of the Saturated Hydraulic Conductivity of Soil Influenced and Exchangeable Sodium Percentage (%ESP)

Sodic soil is one of the most common problems of limiting agricultural production that related on irrigation. Saturated hydraulic conductivity is one of hydrodynamic characteristics in evaluating transmission flow through underground levels. To investigate the role of increment of exchangeable sodium in hydraulic conductivity changes (as characterized by the mobility of water in soil) an experi...