Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China
Bibliographic record
Abstract
The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (Tmax and Tmin), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R2 = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, Tmin, and Tmax emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".