Modeling of Soil Cation Exchange Capacity Based on Fuzzy Table Look-up Scheme and Artificial Neural Network Approach

Abstract

In this study, a new approach is proposed as a modification to a standard fuzzy modeling method based on the table look-up scheme. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, neural network model (feed-forward back propagation network) and fuzzy table look-up scheme were employed to develop a pedotransfer function for predicting soil CEC using easily measurable characteristics of clay and organic carbon. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The value of RMSE and R2 derived by ANN model for CEC were 0.47 and 0.94 respectively, while these parameters for fuzzy table look-up scheme were 0.33 and 0.98 respectively. Results showed that fuzzy table look-up scheme had better performance in predicting and modeling of soil cation exchange capacity than artificial neural network.