부산대학교 도서관

Accurate forecasting of daily evapotranspiration (ET) is essential for enhancing real-time irrigation scheduling and informed decision-making in water resources allocation. This study investigates the intricate relationships between meteorological variables and evapotranspiration (ET) to enhance the accuracy of ET estimation models. Robust correlations were identified, emphasizing the significance of net radiation (Rn) in predicting ET. The study explores three distinct scenarios, incorporating different combinations of weather variables as input. The first scenario incorporates all weather variables, including date and time, as inputs for model development. The second scenario utilizes only Rn as input to predict ET values. In the third and final scenario, all weather variables, along with date and time, are employed as inputs for comprehensive model development. The multivariate linear regression (MLR) model demonstrated exceptional performance when exclusively using Rn, achieving an impressive R2 value of 0.99 in both calibration and validation phases. However, limitations were observed when Rn was excluded, highlighting the necessity of a comprehensive set of input data. Penalized regression models, including ridge regression, LASSO, and ELNET, exhibited improved performance with the inclusion of Rn, supporting the importance of this variable in refining ET estimates. Machine learning models displayed remarkable performance, with most achieving R2 values exceeding 0.95 in scenarios involving extensive input data. The Support Vector Regression (SVR) model faced challenges, indicating potential overfitting in certain scenarios. In scenarios with limited input data, machine learning models exhibited varying performance, with the Random Forest (RF) model emerging as the most robust model with R2 value of 0.99 and 0.84 during the calibration and validation, respectively.