Assessment of Data-Based Models (ANN, ANFIS and SVR) for Estimation of Exchangeable Sodium Percentage (ESP) of Bafra Plain Soils

Abstract

The objective of the present study was to estimate the exchangeable sodium percentage (ESP) of the soil from the Bafra plain utilizing easily determined soil characteristics (EC and pH) with the use of artificial intelligence-based models. A total of 448 soil samples were taken from different points of the study area. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) models were developed and compared. The present database was randomly divided into training and test data sets (70:30). Coefficient of determination (R-2), normalized root mean square error (NRMSE), normalized mean absolute error (NMAE), Nash and Sutcliffe model efficiency (NS) and Akaike's Information Criterion (AIC) were used as statistical performance indicators to assess the accuracy of the models. Present findings revealed that both ANN (R-2 = 0.91, NMAE = 0.21, NRMSE = 0.05, NS = 0.91 and AIC = 191.86) and ANFIS (R-2 = 0.91, NMAE = 0.21, NRMSE = 0.05, NS = 0.91 and AIC = 195.51) models had greater general estimation performance than SVR (R-2 = 0.89, NMAE = 0.49, NRMSE = 0.08, NS = 0.74 and AIC = 334.57) model. Comparative assessments revealed that ANN and ANFIS approaches could successfully be used in estimation of ESP from EC and pH data. It was concluded based on present findings that artificial intelligence-based techniques could reliably be used in estimation of soil ESP as a promising alternative of traditional approaches.