Application of Artificial Intelligence in Modeling of Soil Properties (Case Study: Roodbar Region, North of Iran)

Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental researches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Therefore, in this study indirect methods have been used to estimate cation exchange capacity. Eighty soil samples were collected from different horizons of 26 soil profiles located in the Roodbar region, Guilan Province, North of Iran. Measured soil variables included texture, organic carbon and cation exchange capacity. Then, multiple linear regression, Neuro-Fuzzy and feed-forward back-propagation network were employed to develop a pedotransfer function for predicting soil parameter using easily measurable characteristics of clay and organic carbon. Results showed that Neuro-Fuzzy was superior to artificial neural network and MR in predicting soil property.