Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm / Modélisation d’incertitudes dans la gestion de barrage à court terme grâce à des ensembles flous et à un algorithme génétique
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Abstract Various uncertainties are inherent in modelling any reservoir operation problem. Two of these are addressed in this study: uncertainty involved in the expression of reservoir penalty functions, and uncertainty in determining the target release value. Fuzzy set theory was used to model these uncertainties where the preferences of the decision maker for the fuzzified parameters are expressed as membership functions. Nonlinear penalty functions are used to determine the penalties due to deviations from targets. The optimization was performed using a genetic algorithm with the objectives to minimize the total penalty and to maximize the level of satisfaction of the decision maker with fuzzified input parameters. The proposed formulation was applied to the problem of finding the optimal release and storage values, taking Green reservoir in Kentucky, USA as a case study. The approach offers more flexibility to reservoir decision-making by demonstrating an efficient way to represent subjective uncertainties, and to deal with non-commensurate objectives under a fuzzy multi-objective environment.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it