Inexact Two-Stage Stochastic Robust Optimization Model for Water Resources Management Under Uncertainty
Bibliographic record
Abstract
An inexact two-stage stochastic robust programming model (ITSRP) was developed in this study for dealing with water resources allocation problems under uncertainty. ITSRP was formulated based on integration of interval linear programming (ILP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP) techniques. It could deal with uncertainties expressed as not only probability distributions but also discrete intervals, and could facilitate analyses of the policy scenarios that are associated with economic penalties when the predefined policies were violated. Moreover, the variability measure about the second-stage penalty costs was incorporated into the objective function, such that the trade-off between system economy and stability could be evaluated. The developed model was applied to a hypothetical case of water resources management system. Results demonstrated that the ITSRP model could help decision makers generate stable and balanced water resources allocation patterns, gain in-depth insights into effects of the uncertainties, and analyze trade-offs between system economy and stability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".