Optimizing Water Resources Allocation and Hydropower Generation for Supporting Reservoir Management
Bibliographic record
Abstract
In this paper, a multi-stage fuzzy stochastic programming (MFSP) method is developed to optimize water resources allocation under uncertainties. MFSP method can not only deal with uncertainties expressed as fuzzy sets and probabilistic distributions, but also provide a linkage between the pre-regulated policies and the associated economic implications. Four hydropower generation targets, three inflow levels and five violation risk levels are analyzed. Results indicate that: (i) system benefit would reduce among with the rise of$\alpha$and$\beta$levels, which range from 4.16 to 5.08 × 109US$; (ii) since agriculture is still the largest water user in the future, it is desired to reduce agricultural water consumption through adjusting crop planting structure and water allocation scheme; (iii) inflow levels have significant influence on water allocation pattern. With the growth of inflows, the total allocated water would increase 8.34 × 109m3. Therefore, managers should pay emphasis on the reservoir management in different inflows and consider storing
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".