Estimation of Soil Cation Exchange Capacity using Multiple Regression, Artificial Neural Networks, and Adaptive Neuro-fuzzy Inference System Models in Golestan Province, Iran

Abstract

Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) provide an alternative by estimating soil parameters from more readily available data. In this article, multilayer perceptron (MLP) and radial basis function (RBF) of ANN and ANFIS models were described to estimate soil cation exchange capacity and compared to traditional multiple regression (MR). Moreover, to test the accuracy of previous functions that estimate cation exchange capacity (CEC), five pedotransfer functions (PTFs) were surveyed. The results showed that the accuracies of ANN and ANFIS models were similar in relation to their statistical parameters. It was also found that ANFIS model exhibited greater performance than RBF, MLP, MR, and PTFs to estimate soil CEC, respectively. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting soil CEC. The performance comparisons of used models showed that the soft computing system is a good tool to predict soil characteristics.

Keywords:

• Adaptive neuro-fuzzy inference system
• artificial neural networks
• cation exchange capacity
• multiple regression
• pedotransfer function
• soil characteristics