A novel data-driven solution for sustainable irrigation: Multi-station validation of a three-variable support vector regression model in Batna, Algeria

Abstract

Accurate evaluation of reference evapotranspiration (ETo) is important for effective long-term irrigation planning and water resource management in arid/semi-arid regions. This study evaluates four models: linear regression, Takagi–Sugeno fuzzy inference system, random forest (RF), and support vector regressor (SVR) to estimate daily ETo. The models were trained using ERA5-Land daily climatic variables across 24 meteorological stations in the Batna region (Algeria). To address potential temporal leakage in meteorological time series, we evaluated both random splitting (70% training, 15% validation, and 15% testing) and chronological splitting. The SVR model, utilizing only three automated features-air temperature at 2 m, soil temperature at 0–7 cm, and vapor pressure deficit-demonstrated significantly better performance than the other models at each station, with test root mean square error (RMSE) values ranging from 0.544 mm/day (Tazoult) to 0.780 mm/day (Bitam) and Nash–Sutcliffe efficiency values ≥ 0.91. RF showed severe overfitting, with a 164.93% increase in test RMSE. To further validate SVR’s robustness, we compared it against two additional benchmark models from the literature: a multilayer perceptron (MLP) and a light gradient boosting machine (LightGBM). SVR consistently outperformed both MLP (RMSE: 0.645 mm/day) and LightGBM (RMSE: 0.612 mm/day). Therefore, the results suggest that the SVR model utilizing only three climatic features provides a highly novel, computationally efficient, and accurate platform for predicting daily ETo in arid/semi-arid Mediterranean climates, offering an operational solution for irrigation scheduling in data-scarce environments.

Keywords

Reference evapotranspiration / Machine learning / FAO-56 Penman–Monteith / Semi-arid region / Algeria

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