Validation en temps réel des données hydrométriques
Bibliographic record
Abstract
The managers of hydrological systems take real-time level and discharge measurements on several reservoirs and river reaches. The measurements are frequently tainted with errors that are reflected by uncertainties in decision making and by non-optimal resource management. This article aims at developing a methodology for the validation of levels in real-time. The proposed approach is based on material and analytical redundancy, and uses two models. The first one is spatial and enables linking of the measurements carried out at different stations with the help of a multiple regression equation. The second one is temporal, which enables the determination of variation trend at the different stations with the help of an auto-regressive model. These two models are incorporated into a diagnosis system for breakdowns based on a logic vote principle. Among the values that are measured and estimated by the linear regression model, the one which is the most consistent with the variation trend indicated by the auto-regressive model is selected. The Kalman filter is used to filter the measurements and identify the parameters of the models used in real-time. The proposed methodology turned out to be conclusive when applied to both measured data and synthetic hydrographs.Key words: validation, real-time, material redundancy, analytical redundancy, regressive, auto-regressive, Kalman filter.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".