Data-Driven Estimation of Reference Evapotranspiration in Paraguay from Geographical and Temporal Predictors

Abstract

Reference evapotranspiration (ET0) is a fundamental variable for irrigation scheduling and water management. Conventional estimation methods, such as the FAO-56 Penman-Monteith equation, are of limited use in developing regions where meteorological data are scarce. This study evaluates the potential of machine learning (ML) approaches to estimate ET0 in Paraguay, using only geographical and temporal predictors-latitude, longitude, altitude, and month. Five algorithms were tested: artificial neural networks (ANNs), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGB), and adaptive neuro-fuzzy inference systems (ANFISs). The framework consisted of ET0 calculation, baseline model testing (ML techniques), ensemble modeling, leave-one-station-out validation, and spatial interpolation by inverse distance weighting. ANFIS achieved the highest prediction accuracy (R2 = 0.950, RMSE = 0.289 mm day-1, MAE = 0.202 mm day-1), while RF and XGB showed stable and reliable performance across all stations. Spatial maps highlighted strong seasonal variability, with higher ET0 values in the Chaco region in summer and lower values in winter. These results confirm that ML algorithms can generate robust ET0 estimates under data-constrained conditions, and provide scalable and cost-effective solutions for irrigation management and agricultural planning in Paraguay.