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

Shih-Lun Fang, Yi-Shan Lin, Sheng-Chih Chang, Yi-Lung Chang, Bing-Yun Tsai and Bo-Jein Kuo ()
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Shih-Lun Fang: Department of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan
Yi-Shan Lin: Department of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan
Sheng-Chih Chang: Taiwan Seed Improvement and Propagation Station, Taichung 426017, Taiwan
Yi-Lung Chang: Taiwan Seed Improvement and Propagation Station, Taichung 426017, Taiwan
Bing-Yun Tsai: Taiwan Seed Improvement and Propagation Station, Taichung 426017, Taiwan
Bo-Jein Kuo: Department of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan

Agriculture, 2024, vol. 14, issue 4, 1-20

Abstract: The reference evapotranspiration (ET 0 ) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET 0 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 ET 0 using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET 0 forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET 0 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 ET 0 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 ET 0 , 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 ET 0 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.

Keywords: artificial neural network; long short-term memory; reference evapotranspiration; Penman-Monteith equation; limited meteorological variables (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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