Comparison of K-nearest neighbor and artificial neural network methods for predicting cation exchange capacity of soil

Cation exchange capacity (CEC) measurement is a very expensive and time-consuming method in large scale assessments. It can be an appropriate approach to predict CEC from readily available properties via developing nonparametric models. In the present study, a nonparametric technique has been used for estimating CEC and compared with the most common nonparametric models which is based on artificial neural networks (ANN). 683 soils were selected from central Iran that 120 of them were used as target data and the others (563) were the reference data set. The parameters, clay, silt, sand and organic carbon content were the input independent variables (readily available properties) and the CEC was as an output dependent variable in this work. The results showed that the maximum error (MaxE) in K-NN and ANN techniques were 4.81 and 5.26 cmol+/kg, respectively. Root mean squared error (RMSE) for the K-NN and ANN were 1.51 and 1.53, respectively. This indicated that both methods are able to properly and equally predict CEC. The positive values of mean error (ME) showed that both models tended to underestimate CEC values in samples. The results analysis also showed that the efficiency of models (EF=0.88) were high by the estimation of CEC values in target soils.