Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region
Bibliographic record
Abstract
<title>Abstract</title> As much as accurate streamflow forecasts are important and significant for arid regions, they remain deficient and challenging. An ensemble learning strategy of decomposition-based machine learning and deep learning models was proposed to forecast multi-time-step ahead streamflow for northwest China’s Dunhuang Oasis. The efficiency and reliability of a Bayesian Model Averaging (BMA) ensemble strategy for 1-, 2-, and 3-day ahead streamflow forecasting was evaluated in comparison with decomposition-based machine learning and deep learning models: (<italic>i</italic>), a variational-mode-decomposition model coupled with a deep-belief-network model (VMD-DBN), (<italic>ii</italic>) a variational-mode-decomposition model coupled with a gradient-boosted-regression-tree model (VMD-GBRT), (<italic>iii</italic>) a complete ensemble empirical mode decomposition with adaptive noise model coupled with a deep belief network model (CEEMDAN-DBN), and (iv) a complete ensemble empirical mode decomposition with adaptive noise model with a gradient boosted regression tree coupled model (CEEMDAN-GBRT). Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe coefficient (NSE) values of 0.976, 0.967, and 0.957, the BMA model achieved the greatest accuracy for 1-, 2-, and 3-day ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of the BMA model in yielding consistently accurate streamflow forecasts. Thus, the BMA ensemble strategy could provide an efficient alternative approach to multi-time-step ahead streamflow forecasting for areas where physically-based models cannot be used due to a lack of land surface data. The application of the BMA model was particularly valuable when the ensemble members gave equivalent satisfactory performances, making it difficult to choose amongst them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".