Operation of Hydrosystems under Uncertain Decision Making Environments
Bibliographic record
Abstract
Dealing with uncertainty in planning and management of water resource systems is a challenging task. Modelers of water resource systems are often confronted with uncertainty issues in handling the natural variability of a variety of hydrologic and physical processes, and systems with both stochastic and (not so) deterministic inputs in the modeling process. Approaches to handle imprecise and uncertain aspects of loss functions within optimization frameworks are addressed in this paper. Fuzzy mathematical programming models under uncertain environments are developed to address the issues of uncertainty and imprecision associated with the penalty coefficients and zones respectively. These issues are handled simultaneously in an optimization framework. The formulations are developed using linear and nonlinear programming methods within symmetric and non-symmetric fuzzy environments that are defined by the vague nature of constraints or objective functions or both. The models are applied.to a case study of existing reservoir in the state of Kentucky. Results suggest the utility of using fuzzy set theory concepts for handling problems in uncertain environments that cannot be addressed using traditional probability theory. Also, the use of fuzzy set theory within an optimization framework provides a number of advantages in dealing with the uncertainty associated with economic objectives.
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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".