Modèle de prévision pour la gestion en temps réel : application aux réseaux d'assainissement
Bibliographic record
Abstract
The work presented here aims at developing a flow forecast model dedicated to real-time management. The proposed model is based on the notion of a transfer function for a linear system identified through the Kalman filter algorithm. In a first step, the transfer function model is linked to the Muskingum semi-empirical model; then it is modified to eliminate the autoregressive component. The Kalman filter algorithm allows the parameters of the proposed model to be updated upon the reception of each new measure with respect to the forecast errors observed in real time. To analyze the performance of the proposed model, its results are compared with those obtained using the dynamic wave model and the simplified kinematic wave model. Because of the absence of measured downstream flow values corresponding to the input hydrograph, the results from the dynamic wave model are used as reference values to evaluate the performance of the other models. These results are also used with the addition of noises to simulate measured values and feed, in "real-time," the identification algorithm of the transfer function in order to adjust, a posteriori, its parameters according to its differences in the flow prediction. The results obtained by the transfer function model agree with those obtained by the dynamic model following the three performance criteria employed. The Nash coefficient and the ratio between the peak flows are close to unity in all of the cases. Also, the lag between the peak flows estimated by the two models is negligible.Key words: waste water networks, real-time management, flow propagation models, forecast, transfer function, Kalman filter.[Journal Translation]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".