A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation
Why this work is in the frame
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Bibliographic record
Abstract
A fusion-based framework, in which a particle filter Markov chain Monte Carlo (PFMCMC) data assimilation method was coupled with the hydrological Sacramento Soil Moisture Accounting Model (SAC-SMA), was developed to improve the model’s capacity to predict one-day-ahead runoff. A case study was applied where mean daily precipitation from multiple sources served as forcing data in the data assimilation procedure, while ground station and multiple bias-corrected satellite-based precipitation datasets served as precipitation input datasets. The model training period used six years (2002–2007) of data to determine optimal weights through a genetic algorithm optimization model, while two years (2008–2009) were used to test the model. The proposed framework, applied to a real case study, improved SAC-SMA runoff prediction accuracy by incorporating precipitation datasets from multiple sources in the data assimilation procedure. On average, the PFMCMC-based data assimilation procedure led to a 13.7% improvement in SAC-SMA model performance metrics (NSE, MAB, RMSE, RMSRE, RMRE).
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it