Simulation Model for Optimal Operation of Dokan Dam Reservoir Northern of Iraq
Bibliographic record
Abstract
Due to the limitation of water renewable resources on one hand and increasing growth in consuming water in different parts such as agriculture, industry, urban, and the environment in other hand, face management of these valuable resources to many challenges. Present study attempts to clarify recent condition of the problem and introduce effective management tools in water supply sector. In order to achieve this purpose, simulating model HEC-Res Sim was used for Dokan Dam to study the operational behavior of the reservoir and to investigate the model capability in representing and simulating the real system. The study based on monthly discharge data for the period from 1986 to 2016 measured at the inlet of Dokan Dam reservoir. The results of the current study were compared and evaluated against those counterparts observed data using two statistical metrics, correlation coefficient and Nash- Sutcliff coefficient efficiency. Moreover, an empirical formula was found linking the amount of inflow to the reservoir with the amount of outflow. The results showed that the HEC-ResSim 3.0 performed well in simulating the monthly discharges. Therefore, HEC-ResSim 3.0 could be used for better water system analysis in this study area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".