Prediction of the amount of water at Field Capacity and Permanent Wilting Point Using Artificial Neural Network and Multivariate Regression

Investigation of soil hydraulic properties like permanent wilting point (PWP) and field capacity (F. C) are very important for studding and modeling the soil water and solute transport in soil in which their spatial and temporal variability led to development of indirect methods in prediction of these soil characteristics. Therefore; in the present study in order to evaluate the amount of water at F. C and P. W. P، 63 samples have been taken from 15 pedons in Fashand region. The particle size distribution have been determined by hydrometric method، bulk density by volumetric method (using undisturbed clods)، saturation percentage by weight and percentage of water at F. C and P. W. P by using pressure plate apparatus. We applied the artificial neural network (ANN)، multivariate regression (MR) methods and used several pedotransfer functions (PTFs) to predict the F. C and P. W. P parameters، using the easily measurable characteristics of clay، silt and sand percentage، bulk density and water at saturation percentage. The results showed that the ANN method give the best results followed by MR method and finally the PTFs. Regarding the PTFs، the classic are showed better results relative to parametric and point PTFs. In conclusion، the results of this study showed that، training is very important in increasing the model accuracy of one region.