Interval-parameter chance-constrained fuzzy multi-objective programming for water pollution control with sustainable wetland management
Bibliographic record
Abstract
Water pollution control plays a significant role in the water quality management of wetland ecosystems. In this study, an interval-parameter chance-constrained fuzzy multi-objective programming (ICFMOP) model for assisting water pollution control within a sustainable wetland management system under uncertainty was developed. The proposed ICFMOP approach not only effectively handled the uncertainties and complexities in the water pollution control management systems, it also allowed decision makers to adjust the fuzzy objective control decision variable to satisfy multiple holistic and interactive objectives. The ICFMOP model developed was then applied to a wetland water pollution control case study to assist the planning of regional wetland eco-environmental sustainability. Interval solutions of the compromise decision alternatives associated with different risk levels of constraint violations were obtained. The results were helpful for decision makers to identify desirable strategies under various social-economic, environmental and system-reliability constraints with the highest system benefits and the lowest water pollutant discharge and eco-environment impact. Moreover, tradeoffs between the multiple objectives and the constraint-violation risks could be evaluated.
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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 | 0.001 |
| 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".