Water quality management and control has high importance in planning and developing of water resources. This study investigated application of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACO_R) and Differential Evolution (DE) in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters of Gorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the quality parameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DE with R² values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55, respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction of SAR (R² = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the best performance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentioned algorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.