Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia
Bibliographic record
Abstract
Abstract Boukan Dam reservoir is the largest infrastructure constructed on the Zarineh-Roud River regulating streamflow for different uses including supplying water to Lake Urmia, the second largest salt lake in the world. This paper presents a forecast-based adaptive real-time optimal operation model (ARTOM) for Boukan reservoir with the aim of maximizing releases feeding the lake while meeting other needs such as irrigation, industrial, and domestic uses. Adaptive neuro-fuzzy system-based inflow-to-reservoir forecasts are used in the ARTOM to determine optimal releases from the reservoir for future months up to the end of a year, but only the current period's release is applied. At the beginning of the next period, the forecasts are updated, and the procedure is repeated until the last period of the year. Additionally, the optimal terminal end-of-year reservoir storage volume is a dynamic updating input to the ARTOM, which is estimated from the results of a long-term reservoir operation optimization model. The ARTOM performance is tested against the last nine-year monthly data not utilized for training the forecast module. Results demonstrate that the ARTOM attains an objective function value very close to the best possible value that can ever be reached by utilizing an ideal operation model, benefiting from perfect foresight on future streamflows.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".