Using Optimization to Investigate the Adaptive Operation of Reservoirs under the Context of Climate Crisis
Bibliographic record
Abstract
This study investigates the climate change impacts on the performance of the Thac Mo reservoir in Vietnam throughout the release duration from December to June of the following year. The adaptative optimization operating rules during two periods, 2029–2064 and 2064–2099, were analyzed. Precipitation and temperature of three shared socioeconomic pathway (SSP) scenarios (SSP1–2.6, SSP2–4.5, and SSP5–8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) derived from the global climate model (GCM) Meteorological Research Institute Earth System Model version 2.0 (MRI-ESM2–0) were used for inflow prediction. Due to the decreased inflow, the current operating rule would almost lead to a decrease in hydropower production of two periods of SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios of −3.9, 0.7, −7.6, −6.7, −6.3, and 7.0% and an increase in water scarcity, with the respective amount of water deficit by 20.4, 6.8, 33.2, 31.3, 30.2, and 28.3% compared to the base period (1987–2022). The improved power production of 7.1, 7.1, 7.3, 6.2, 6.9, and 6.8% and reduced water shortage of −40.2, −42.7, −37.2, −43.4, −40.8, and −39.0% can be achieved by applying the nondominated sorting genetic algorithm II (NSGA-II) optimization technique under different periods and scenarios. This approach has the potential to mitigate climate change effects on future reservoir operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".