Planning Water Resources Allocation Under Multiple Uncertainties Through a Generalized Fuzzy Two-Stage Stochastic Programming Method
Bibliographic record
Abstract
In this study, a generalized fuzzy two-stage stochastic programming (GFTSP) method is developed for planning water resources management systems under uncertainty. The developed GFTSP method can deal with uncertainties expressed as probability distributions, fuzzy sets, as well as fuzzy random variables. With the aid of a robust stepwise interactive algorithm, solutions for GFTSP can be generated by solving a set of deterministic submodels. Furthermore, the possibility information (expressed as fuzzy membership functions) can be reflected in the solutions for the objective function value and decision variables. The developed GFTSP approach is also applied to a water resources management and planning problem to demonstrate its applicability. Solutions of decision variables and objective function value are expressed as fuzzy membership functions, reflecting the fluctuating ranges of decision alternatives under different plausibilities. And thus, the water alternatives can be directly derived from the obtained fuzzy membership functions when the preferred α value is predefined by decision makers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".