A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model
Bibliographic record
Abstract
Abstract Accurate forecasting of mid to long-term runoff is essential for water resources management and planning. However, the traditional model can’t predict well and the precision of runoff forecast needs to be further improved. Here, we proposed a noval data-driven model called RLMD -SMA-GRU for mid to long-term runoff prediction in three hydrographic stations (Heishiguan, Baimasi and Longmenzhen) of Yiluo River Watershed (middle of China) using monthly runoff data from 2007 to 2022. The results showed that (1) the new data-driven model (RLMD -SMA-GRU) had the highest monthly runoff prediction accuracy. Both RLMD and SMA can improve the prediction accuracy of the model (NSE=0.9466). (2) The accuracy of Models in wet season outperformed in dry season. (3) The hydrological stations with large discharge and stable runoff sequence have better forecasting effect. The RLMD-SMA-GRU model has good applicability and can be applied to the monthly runoff forecast at watershed scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".