ABSTRACT Infiltration is crucial in the hydrological cycle, serving as the primary process that increases soil moisture. This study investigates the estimation of soil infiltration rate (IR) using various techniques, including the group method of data handling, Gaussian process, support vector machine, artificial neural network (ANN), and multivariate adaptive regression splines. A total of 190 field observations were collected from Alashtar sub-watersheds in Lorestan, Iran. About 70% of the observations were used for model preparation, while 30% were used for validation. The input variables for the study are time, sand, clay, silt, pH, electrical conductivity, moisture content, soil bulk density, porosity, calcium carbonate, phosphorus, organic carbon, organic matter, nitrogen, and temperature, while IR is the output variable. Obtained results indicate that the ANN has a higher accuracy with coefficient of correlation values of 0.9366, 0.8624, mean absolute error values as 0.0607, 0.1000, Nash–Sutcliffe model efficiency values of 0.8732, 0.7350, the scattering index values of 0.3108, 0.5003, and the Legates and McCabe's index values of 0.6585, 0.5654 by using training and testing datasets, respectively. A sensitivity analysis highlighted that time is the parameter that most influences estimating the IR. The study underscores the precision of ANN in predicting soil IRs and the need for soft computing models in hydrological models to improve accuracy and reliability in IR prediction.
Read full abstract