Estimating Saturated Hydraulic Conductivity In Spatially Variable Fields Using Neural Network Ensembles

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

Arti fi cial Neural Network Estimation of Saturated Hydraulic Conductivity

cial neural networks have become a common tool for modeling complex “input–output” dependencies. In the past, neural network models have been used as a special class of pedotransfer functions (PTFs) using feed-forward back propagation or radial basis functions to approximate any continuous (nonlinear) function (Hecht-Nielsen, 1990; Pachepsky et al., 1996; Schaap and Bouten, 1996; Minasny and Mc...

estimating the saturated hydraulic conductivity of granular material, using artificial neural network, based on grain size distribution curve

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Estimating the saturated hydraulic conductivity in a spatially variable soil with different permeameters: a stochastic Kozeny–Carman relation

The spatial variability of the saturated hydraulic conductivity (Ks) of a greenhouse banana plantation volcanic soil was investigated with three different permeameters: (a) the Philip-Dunne field permeameter, an easy to implement and low cost device; (b) the Guelph field permeameter; (c) the constant head laboratory permeameter. Ks was measured on a 14×5 array of 2.5 m×5 m rectangles at 0.15 m ...

Scale-Dependent Pedotransfer Functions Reliability for Estimating Saturated Hydraulic Conductivity

Saturated hydraulic conductivity Ksat is a fundamental characteristic in modeling flow and contaminant transport in soils and sediments. Therefore, many models have been developed to estimate Ksat from easily measureable parameters, such as textural properties, bulk density, etc. However, Ksat is not only affected by textural and structural characteristics, but also by sample dimensions e.g., i...