Introducing time series features based dynamic weights estimation framework for hydrologic forecast merging
Bibliographic record
Abstract
• Time series features (TSFs) based weights (TSF-Ws) estimation framework is proposed for hydrologic forecast merging. • TSF-Ws significantly improve forecast accuracy, especially for longer lead times. • TSF-Ws-based merging shows better accuracy skills for high and low flow. • The TSF-Ws approach reduces the uncertainty bound for peak flow predictions. Accurate and reliable hydrologic forecasting through multi-model ensemble averaging is crucial for reducing uncertainty, which aids in effective water resources management and flood risk mitigation. This study addresses the research gap of the limited application of time-varying weights in hydrologic forecast merging, as existing methods rely on weights that do not adapt to changes in model performance over time. We propose a novel framework utilizing time series features (TSFs) of daily streamflow and Bayesian model averaging (BMA) to dynamically adjust merging weights, referred to as TSF-Ws. The methodology involves generating ensemble forecasts, adjusting weights dynamically using TSFs, and comparing the accuracy of these forecasts with traditional streamflow-based weights, referred to as Q-Ws, merging across different forecast horizons. The results demonstrate that TSF-Ws significantly improve forecast performance, particularly for longer lead times, indicating more accurate and reliable deterministic and probabilistic forecasts. Moreover, TSF-Ws based merging achieves higher performance than Q-Ws for deterministic high and low flow forecasts. Furthermore, this newly developed approach reduces the uncertainty bound for probabilistic peak flow predictions. Overall, the proposed TSF-Ws estimation framework can serve as a robust tool for enhancing hydrologic forecast merging, providing significant improvements in accuracy and reliability over traditional methods. These improvements have important implications for water resource management and flood risk assessment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".