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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