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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.

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Wind speed (WS) is an important factor in wind power generation. Because of this, drastic changes in the WS make it challenging to analyze accurately. Therefore, this study proposed a novel framework based on the stacking ensemble machine learning (SEML) method. The application of a novel framework for WS modeling was developed at sixteen stations in Iran. The SEML method consists of two levels. In particular, eleven machine learning (ML) algorithms in six categories neuron based (artificial neural network (ANN), general regression neural network (GRNN), and radial basis function neural network (RBFNN)), kernel based (least squares support vector machine-grid search (LSSVM-GS)), tree based (M5 model tree (M5), gradient boosted regression (GBR), and least squares boost (LSBoost)), curve based (multivariate adaptive regression splines (MARS)), regression based (multiple linear regression (MLR) and multiple nonlinear regression (MNLR)), and hybrid algorithm based (LSSVM-Harris hawks optimization (LSSVM-HHO)) were selected as the base algorithms in level 1 of the SEML method. In addition, LSBoost was used as a meta-algorithm in level 2 of the SEML method. For this purpose, the output of the base algorithms was used as the input for the LSBoost. A comparison of the results showed that using the SEML method in WS modeling greatly affected the performance of the base algorithms. The highest correlation coefficient (R) in the WS modeling at the sixteen stations using the SEML method was 0.89. The SEML method increased the WS modeling accuracy by >43%.