A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".