Evaluation of Random Forest model to calculate potential Evapotranspiration using limited meteorological data (study area: Ardabil Plain)

As the global demand for water resources increases, the reduction in water loss, including Evapotranspiration, becomes more obvious. Although many models have been developed to predict evapotranspiration, no universally accepted model for all climate regions has been established. Several soft computational models have been created to circumvent the constraints of empirical models and accurately predict ET. Soft computing models typically require less data and are applicable across various climatic zones. This study aimed to analyze how well two Random Forest models and Multiple Linear Regression could predict ETo in the Ardabil plain region. Meteorological data from the Iranian Meteorological Organization were used to calculate the reference evapotranspiration from 2014 to 2016. In constructing the model, data from 4 meteorological stations were combined to generate a random time series, while the fifth station was reserved for evaluating the models. The assessment metrics used comprised RMSE, R2, and NSE. The RF model achieved higher accuracy with R2, NSE, and RMSE values of 0.74, 0.743, and 8.20 mm, respectively, compared to the MLR model. The results demonstrate that random forest models are reliable tools for forecasting ETo with minimal climate data. In general, using the results of this study and other similar research, we conclude that RF and MLR models simulate potential evapotranspiration with acceptable accuracy but are sensitive to the number of input parameters.