Zohre Ebrahimi-Khusfi

With intense human activity causing constant environmental change, there is a greater need than ever to have accurate and frequently updated soil information. Traditional soil maps are large-scale polygon maps of soil type and implicitly assume no variation in soil properties within polygons. DSM approaches can increase the accuracy of modelling. Therefore, the current study was conducted to evaluate the applicability of a classic adaptive neuro-fuzzy inference system (ANFIS) and three hybridized ANFIS models (i.e. particle swarm optimization— PSO, the genetic model—GA and the artificial bee colony model—ABC) for predicting eight soil properties in western Iran. The ANFIS+ABC and ANFIS+PSO approaches showed the best performance in predicting soil properties such as soil organic carbon (SOC) and total nitrogen (N). The ABC+ANFIS-hybrid model increased R2 values by 0.32, 0.24, 0.23 and 0.37 and decreased RMSEs by 11%, 32.7 mg kg−1, 0.08 mmhos cm−1 and 0.3% when predicting SOC, K, EC and SP, respectively, compared with the ANFIS model alone. Also, the use of the PSO+ANFIS-hybrid model led to an increase in R2 values of 0.44, 0.29, 0.34 and 0.36 and a decrease in RMSEs of 0.02, 0.29 ppm, 0.34% and 0.36% for predicting N, P, pH and TNV, respectively, compared with the ANFIS model alone. In general, the ANFIS+ABC and ANFIS+PSO improved the prediction accuracies for SOC and N by 32% and 24%, respectively, compared with the classic ANFIS model. As a whole, our findings demonstrated the ability of hybrid models for improving the accuracy of the ANFIS approach for predicting soil features in western Iran, which supports our main hypothesis