Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

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

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

soil hydraulic properties such as saturated and unsaturated hydraulic conductivity play an important role in environmental research. since direct measurement of these soil hydraulic properties is time-consuming and costly, indirect methods such as pedotransfer functions and artificial neural networks (ann) were developed based on readily available parameters. in this study, the use of ann to pr...

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

monthly runoff estimation using artificial neural networks

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...