Artificial Neural Networks to predict decreasing saturated hydraulic conductivity in soils irrigated with saline-sodic water

Abstract: Multilayer Artificial Neural Networks (ANNs) with the backpropagation algorithm were used to estimate the decrease in relative saturated conductivity due to an increase in sodicity and salinity. Data from the literature on the relative saturated hydraulic conductivity measured using water having levels of sodicity and salinity in different types of semiarid soils were used. The clay content of these soils is predominantly montmorillonite. The input data consisted of clay percentage, cation exchange capacity, electrolyte concentration, and estimated soil exchangeable sodium percentage at equilibrium stage with the solution applied. The data was divided into three groups randomly to meet the three phases required for developing the ANNs model (i. e. training, evaluation, and testing).The activation function selected was the TANSIG layer in the middle, while the exit function was the PURELIN layer. The comparisons between the experimental and predicted data on relative saturated hydra.... https://journals.ub.uni-koeln.de/index.php/JNRD/article/view/691

Show object

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Artificial Neural Networks to predict decreasing saturated hydraulic conductivity in soils irrigated with saline-sodic water ; volume:4 ; year:2014 ; Journal of natural resources and development ; 4 (2014)

Contributors, Places and Time

• Creator: Daw Ezlit, Younes ; Ekhmaj, Ahmed Ibrahim ; Elaalem, Mukhtar Mahmud

Further information

• DOI: 10.5027/jnrd.v4i0.05
• URN: urn:nbn:de:101:1-2022053112043588529147
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.