Uncertainty-aware and interpretable machine learning for reference evapotranspiration in contrasting climates of Iran

Tosan, Moein; Shayeghi, Afshin; Bakhtar, Aydin; Mardani, Mahsa; Naseri, Mahdi

doi:10.22098/mmws.2026.19337.1782

Uncertainty-aware and interpretable machine learning for reference evapotranspiration in contrasting climates of Iran

مقالات آماده انتشار

نوع مقاله : پژوهشی

نویسندگان

¹ Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

² Department of Geography & Environmental Sustainability, University of Oklahoma, Norman, OK, USA

³ Department of Civil Engineering, University of Texas at Arlington, Arlington, TX, USA

⁴ Department of Civil Engineering, College of Engineering, University of Birjand, Birjand, Iran

10.22098/mmws.2026.19337.1782

چکیده

Accurate estimation of reference evapotranspiration (ET_o) is indispensable for precision irrigation and sustainable water resource management, yet the lack of physical interpretability in advanced machine learning models limits their operational adoption. This study proposes a systematic framework integrating the state-of-the-art categorical boosting (CatBoost) algorithm, Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to predict daily ET_o across three contrasting climatic classifications in Iran: arid, semi-arid, and humid. By benchmarking CatBoost against extreme gradient boosting (XGBoost) and Random Forest under various sensor-availability scenarios, we demonstrated the superior robustness and generalization capability of the gradient boosting framework (CatBoost achieved R2 > 0.99 and RMSE ranging from 0.06 to 0.13 mm/day across all climates), particularly in capturing peak evaporative demands. Beyond mere prediction, the integration of explainable AI revealed a distinct climatic divergence in hydrological drivers; while aerodynamic forces, specifically wind speed, act as the primary accelerator of ET_o in arid environments, the process is predominantly energy-limited and driven by temperature and solar radiation in humid regions. Furthermore, the study identified critical non-linear environmental thresholds that trigger rapid escalations in water demand, a dynamic often missed by linear empirical equations. Uncertainty analysis using Quantile Regression further confirmed the model's reliability in handling stochastic climatic extremes (achieving a Prediction Interval Coverage Probability of 88.1-91.4% and narrow interval widths). Practically, our findings offer a cost-effective roadmap for agricultural monitoring, suggesting that while low-cost, temperature-based sensors suffice for humid and semi-arid regions, the inclusion of aerodynamic sensors is non-negotiable for accurate irrigation scheduling in arid zones. This research contributes to bridging the gap between predictive accuracy and physical interpretability, offering a methodological blueprint for optimizing hydro-meteorological networks in data-scarce regions.

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