ABSTRACT
Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However correlation among soil hydraulic properties such as the relationship between saturated soil water content θS and saturated soil hydraulic conductivity Ks as function of soil depth is in stochastic pattern. On the other hand, soil–water profile process is also believed to be highly nonlinear, time-varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing Artificial Neural Networks ANN model was proposed and investigated to map the soil-water profile in terms of Ks and θS with respect to the soil depth d. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves.The proposed ANN model was examined utilizing 49 records data collected from field experiments. The results showed that the ANN model has the ability to detect and extract the stochastic behaviour of the water in the soil and draw the water-soil profile with relatively high accuracy.
