Akademik Veri Yönetim Sistemi
Irrigation and Drainage, 2026 (SCI-Expanded, Scopus)
Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data-limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters separately or focus on a single seasonal Kc value rather than stage-specific coefficients. This study presents an integrated machine learning framework that simultaneously estimates monthly ET0 and wheat stage-specific crop coefficients (Kc_ini, Kc_mid, Kc_end) using long-term district-level meteorological data from Türkiye. A total of 19 algorithms, including linear models, tree-based ensembles, boosting methods, support vector machines (SVR) and deep learning models, were evaluated using 10-fold cross-validation and standard performance metrics. The results showed that linear models provided the most consistent performance for monthly and annual ET0 estimation (R2 ≈0.85–0.87), while nonlinear ensemble models achieved superior accuracy for Kc prediction (R2 > 0.95). Feature importance analysis identified solar radiation and temperature as dominant ET0 drivers, whereas elevation and relative humidity were key controls for stage-specific Kc. The proposed framework provides a scalable approach for regional agricultural water management and supports model selection under data-limited conditions.