Integration of Hydrologic Gray Model with Global Search Method for Real-Time Flood Forecasting
Bibliographic record
Abstract
This paper presents a hydrologic gray model integrated with a global search method to improve the accuracy of real-time flood forecasting for two watersheds. The model’s applicability is evaluated by comparing the runoff forecasts to the observed values. The model’s accuracy is compared with the accuracy of two base models that employ multiple regression equations and the model capability is verified in real situations. The model parameters are corrected by combining the gray system parameters. The fifth-order differential equation is adopted to represent the characteristics of the study watersheds. The statistical values between the observed values and the runoff forecasts in calibration and validation indicate that the simulations are in close agreement with the observations. The model provides more consistent and satisfactory runoff forecasts than the multiple regression models across all flow ranges; the accuracy of the runoff forecasts varies according to hydrograph stages and lead times. These results demonstrate that the proposed model is able to reasonably forecast runoff with 1–6 h of lead time for the two study watersheds.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".