Optimal Rule Curves for Operation the Euphrates River Concerning Tharthar Reservoir as a Case Study
Bibliographic record
Abstract
In this study, the Discrete Differential Dynamic Programming (DDDP) method is utilized to identify the optimum rule curves and policies for the Tharthar Reservoir by adopting an objective function to reduce the release and storage losses.The input data for the optimization model represents the historical data for the Tharthar Reservoir within the Euphrates River Basin from October 2000 to September 2021.The years within this period will be categorized as two sequential wet and dry years for the reservoir operation through the development period of October 2022 to September 2059.In the first scenario, TH1, the plan is unsafe during the planning period of the operation because there is a high deficit in storage and outflow.The summation of the deficit in storage and outflow after applying the TH1 scenario is equal to 52671 million cubic meters (MCM).The second scenario, TH2, is an alternative scenario for operating the Tharthar Reservoir.The summation deficits from using TH2 are equal to 13071 MCM for storage and outflow.The results simulated for the monthly storage were compared with the measured data for a reliability test.Also, the projected water supply calculated by the optimization model by DDDP was compared with the WEAP model.From this comparison, the three statistical parameters, R 2 , NSE, and RSR, were evaluated as an acceptable level and a good agreement of the DDDP and WEAP model performance.
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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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".