نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مهندسی آب دانشکده کشاورزی و منابع طبیعی دانشگاه محقق اردبیلی، اردبیل، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
As the global demand for water resources grows, it is becoming more apparent that reducing water losses, including evapotranspiration, is crucial. While there are many models to predict evapotranspiration, there is no agreement on a universally accepted model for all climate regions. Several soft computational models have been created to circumvent the constraints of empirical models and accurately predict ET. Soft computing models typically necessitate 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 Iran Meteorological Organization were used to calculate reference evapotranspiration from 2014 to 2016. In constructing the model, data from 4 meteorological stations was combined to generate a random time series, while the fifth station was reserved for evaluating the models. The assessment metrics utilized 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 current research demonstrated that random forest models are dependable for forecasting ETo with minimal climate data. In general, using the results of this research and other similar research, it can be said that RF and MLR models simulate potential evapotranspiration with acceptable accuracy, but are sensitive to the number of input parameters.
کلیدواژهها [English]
)