نتایج جستجو برای: Saturated hydraulic conductivity Neural Network Methods (ANN) Cokriging

تعداد نتایج: 2435790  

2007
P. L. G. Vlek,

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...

Journal: :Soil Science Society of America Journal 2006
KambanParasuraman, AminElshorbagy, Bing ChengSi,

Journal: :Journal of Civil Engineering, Science and Technology 2009
LimD.K.H, KolayP.K.,

Journal: :Water 2021
JosuéTrejo-Alonso, CarlosFuentes, CarlosChávez, AntonioQuevedo, AlfonsoGutierrez-Lopez, BrandonGonzález-Correa,

2017
Hyoun-Tae Hwang, Sung-Wook Jeen, Ayman A. Suleiman, Kang-Kun Lee,

This study compares saturated hydraulic conductivities (Ks) of three sandy soils such as coarse, medium and fine sand. The Ks was obtained using three different methods: empirical methods based on the grain size analysis, the relative effective porosity model (REPM), and breakthrough curve analyses based on tracer tests. Column drainage tests were performed to characterize the water retention p...

Journal: :Water 2017
Hyoun-TaeHwang, Sung-WookJeen, AymanSuleiman, Kang-KunLee,

R. Sedghi, S. Bairami, Y. Hoseini,

ABSTRACT-Determining hydraulic conductivity of soil is difficult, expensive, and time-consuming. In this study, Algorithm Genetic and geostatistical analysis and Neural Networks method are used to estimate soil saturated hydraulic conductivity using the properties of particle size distribution. The data were gathered from 134soil profiles from soil and lander form studies of the Ardabil Agricul...

Journal: :Canadian Journal of Soil Science 1985
D. M.LEE, D. E.ELRICK, W. D.REYNOLDS, B. E.CLOTHIER,

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