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Modeling daily reference evapotranspiration using SVR machine learning algorithm with limited meteorological data in Dar-el-Beidha, Algeria

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Salah Z. , Khaled B.

tom Vol. 72, no. 3

2009--2025

Reference evapotranspiration (ETo) is a critical water resource management parameter, including irrigation scheduling and crop water requirements. Because large uncertainties in estimating ETo can result in equally large uncertainties in determining water budgets and crop water requirements, and vice versa, accurate determination of ETo can be challenging when direct measurement and estimation with the Penman-Monteith (FAO-56-PM) semi-empirical equation of the food and agriculture organization (FAO) is not possible. Indeed, this study explores the use of the support vector regression machine learning algorithm (SVR) to predict daily ETo with limited measured inputs. It is the first time that Julian Day (J) is included as an input to improve prediction accuracy. Ten years of meteorological data collected at the Dar-El-Beidha weather station in Algeria are used, with maximum, minimum, and mean air temperatures (TM, tm, and T), mean relative humidity (RH), mean wind speed (u2), and sunshine duration (n) as inputs, as well as J and extraterrestrial solar radiation (Ra) as auxiliary variables, and the ETo-FAO-56-PM values as target outputs. Several SVR models are developed using different combinations of inputs, and their performance is assessed relative to ETo-FAO-56-PM values. Empirical equations are also used for comparison, and several evaluation metrics are employed, including root mean square error (RMSE), mean absolute percentage error (MAPE), determination coefficient (R2), RMSE-standard deviation ratio (RSR), Nash-Sutcliffe efficiency coefficient (NSE), and Willmott’s refined index (WI). The results show that the SVR models utilizing limited meteorological inputs in addition to J and/or Ra predicted ETo accurately and outperformed their corresponding estimates using empirical equations, radial basis function neural networks (RBFNN), and adaptive neuro-fuzzy inference systems (ANFIS) models obtained in previous studies. The RMSE ranged from 0.28 to 0.72 mm/day, R2 from 0.86 to 0.98, MAPE from 7 to 19%, RSR from 0.15 to 0.38, NSE from 0.86 to 0.98, and WI from 0.65 to 0.87. These findings could provide useful solutions for ETo estimation issues in areas with sparse data and agro-climatic conditions similar to those of Dar-El-Beidha.