Data-Driven Modeling of FAO-56 Penman–Monteith Reference Evapotranspiration Using Limited Meteorological Parameters through Artificial Neural Networks

Abstract

Accurate estimation of reference evapotranspiration (ET0) is crucial for irrigation planning and sustainable water management. This study developed Artificial Neural Network (ANN) models to simulate daily FAO-56 Penman-Monteith ET0 using limited meteorological inputs at Junagadh station, Gujarat, India. The Gamma Test was employed to identify optimal input combinations, revealing that maximum temperature (Tmax), wind speed (WS), solar radiation (SR), and relative humidity (RHmean) were the most influential predictors. ANN models with various input configurations (one to six variables) were trained and evaluated using statistical indicators such as RMSE, R², NSE, MAPE, and Willmott’s Index (WI). Results showed that the ANN model with three inputs (Tmax, WS, SR) achieved RMSE = 0.4722 mm/day, R² = 0.9463, NSE = 0.9029, and MAPE = 9.70%, while the four-input model (Tmax, RHmean, WS, SR) yielded RMSE = 0.4504 mm/day, R² = 0.9652, NSE = 0.9116, and MAPE = 8.56%. Models with more than four inputs offered only marginal improvement, indicating that three or four parameter combinations provide optimal accuracy with computational efficiency. The findings confirm that ANN can reliably replicate the nonlinear dynamics of ET0 and serve as a viable alternative to the FAO-56 PM method in data-scarce regions, offering accurate and efficient ET0 prediction for irrigation scheduling and water resource planning. The developed ANN model can serve as an efficient decision-support tool for irrigation scheduling and water resource management in arid and semi-arid regions with limited climatic data availability.