Comparison of Machine Learning Techniques and Spatial Distribution of Daily Reference Evapotranspiration in Turkiye

Abstract

Reference evapotranspiration (ET0) estimates are commonly used in hydrologic planning for water resources and agricultural applications. Last 2 decades, machine learning (ML) techniques have enabled scientists to develop powerful tools to study ET0 patterns in the ecosystem. This study investigated the feasibility and effectiveness of three ML techniques, including the k-nearest neighbor algorithm, multigene genetic programming, and support vector regression (SVR), to estimate daily ET0 in Turkiye. In addition, different interpolation techniques, including ordinary kriging (OK), co-kriging, inverse distance weighted, and radial basis function, were compared to develop the most appropriate ET0 maps for Turkiye. All developed models were evaluated according to the performance indices such as coefficient of determination (R-2), root mean square error (RMSE), and mean absolute error (MAE). Taylor, violin, and scatter plots were also generated. Among the applied ML models, the SVR model provided the best results in determining ET0 with the performance indices of R-2 = 0.961, RMSE = 0.327 mm, and MAE = 0.232 mm. The SVR model's input variables were selected as solar radiation, temperature, and relative humidity. Similarly, the maps of the spatial distribution of ET0 were produced with the OK interpolation method, which provided the best estimates.