Assessment of Regression, Support Vector Machine, and Gene Expression Programming Transfer Functions to Predict Soil Humidity Parameters in Arasbaran Plain

Using transfer functions to predict soil moisture parameters has been considered strictly a scientific and economical method among researchers. In this research, field capacity (FC) and permanent wilting point (PWP) of soil were predicted using classic regression (linear and non-linear), support vector machine (SVM) algorithm, and gene programming expression (GEP) algorithm based on three performance assessment criteria as determination of coefficient (R2), root mean square error (RMSE), and standardized developed discrepancy Ratio (DDR) in the Arasbaran plain in the northwest of Iran. Independent parameters were determined as clay percent (Cl), silt percent (Si), gravel percent (Sa), organic carbon (OC), bulk density (ρb), and actual density (ρs) which (S, ρb, ρs) and (ρb, ρs) were opted to predict FC and PWP using Gamma test, respectively. The results showed that each three transfer functions are capable to simulate FC and PWP parameters but the SVM algorithm is the superior predictor among the three functions so the values of (R2, RMSE, and DDRmax) of training and testing phases for FC were obtained (0.9908, 0.5517, 17.50), (0.9785, 0.7004, 11.62) and those of PWP were calculated (0.9782, 0.5764, 2.85) and (0.8389, 1.187, 3.09), respectively.