comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (case study: ziaran region)

investigation of soil properties like cation exchange capacity (cec) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. pedotransfer functions (ptfs) provide an alternative by estimating soil parameters from more readily available soil data. 70 soil samples were collected from different horizons of 15 soil profiles located in the ziaran region, qazvin province, iran. then, multivariate regression and neural network model (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. the performance of the multivariate regression and neural network model was evaluated using a test data set. in order to evaluate the models, root mean square error (rmse) was used. the value of rmse and r2 derived by ann model for cec were 0.47 and 0.94 respectively, while these parameters for multivariate regression model were 0.65 and 0.88 respectively. results showed that artificial neural network with seven neurons in hidden layer had better performance in predicting soil cation exchange capacity than multivariate regression.