Accurate estimation of soil moisture content is necessary for optimal management of water and soil resources. Soil moisture is an important variable in the hydrologic cycle, which plays an important role in the global water and energy balance due to its impact on hydrological, ecological, and meteorological processes. The purpose of the present study was to explore a newly developed hybrid intelligent model for simulating soil moisture content. A hybrid adaptive neuro fuzzy inference system (ANFIS) model-grey wolf optimization (GWO) algorithm was designed here and validated against the neural network (ANN), support vector regression (SVR) and standalone ANFIS models. The models input parameters were di-electric constant, soil bulk density, clay content and organic matter of 1155 soil samples. Various statistical indices were employed to evaluate the performances of the applied models. For a reliable ranking of models, the Global Performance Indicator (GPI) was utilized, which is a 5-agent index. The results evidenced the feasibility of the developed hybrid ANFIS-GWO model with superior simulation results. At the testing stage, the MAE and SI values for the ANFIS-GWO were 1.468% and 0.098, respectively, which indicated the superiority of the ANFIS-GWO compared to the ANFIS-Fuzzy C mean (MAE=6.427%, SI=0.354), and ANFIS-sub clustering (MAE=2.137%, SI=0.141) models. Based on the GPI, the ANFIS-GWO model was ranked as the best model, followed by the standalone ANFIS and SVR models, while the worst accuracy was attained through ANN model. The ANFIS-GWO model improved the simulation accuracy by 48% and 50%, respectively, compared to the standalone ANFIS and SVR models. In addition, based on the GPI, the ANFIS-GWO model presented an enhancement of about 77 percent compared to the ANN model. The high accuracy of the ANFIS-GWO model compared to the standalone ANFIS model represents the performance of the GWO algorithm for escaping local optima, which makes the ANFIS-GWO as a powerful tool for estimating soil moisture. Overall, the explored hybrid intelligent models demonstrated a reliable pedotransfer function of soil moisture where it can contribute to several geo-sciences engineering principles.
