Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables

Abstract

The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET0 using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET0 forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET0 estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET0 forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET0, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within ±5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET0 forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications.