Multitemporal river flow discharge prediction: A new framework for integrated environmental management and flood control
Bibliographic record
Abstract
of 0.819 and an NSE of 0.818 for the maximum monthly flow. Additionally, compared to the CNN approach, the AUGMDH model yields lower AIC values across all the cases (AIC: 37,744 for daily, 2144 for mean monthly, and 2543 for maximum monthly), indicating a better balance between simplicity and accuracy. In terms of uncertainty analysis, the AUGMDH model exhibits lower uncertainty values (i.e., 2.77 for daily flow, 2.31 for mean monthly flow, and 2.46 for maximum monthly flow estimates) than the CNN models do (i.e., 2.78 for daily flow, 2.48 mean monthly flow, and 2.66 for maximum monthly flow estimates). The findings indicate that the AUGMDH model provides a more robust and reliable solution for riverine flood estimation, outperforming the CNN models across all major performance metrics, including accuracy, reliability, and computational efficiency.
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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 | 0.001 | 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".