Possibilistic Stochastic Water Management Model for Agricultural Nonpoint Source Pollution
Bibliographic record
Abstract
Agricultural activities are the main contributors of nonpoint source water pollution within agricultural systems. In this study, a possibilistic stochastic water management (PSWM) model is developed and applied to a case study of water quality management within an agricultural system in China. This study is a first application of hybrid possibilistic chance-constrained programming approach to nonpoint source water quality management problems within an agricultural system. Hybrid uncertainties with the synergy of fuzzy and stochastic implications are effectively characterized by the PSWM model with the following advantages: (1) it improves upon the existing possibilistic and chance-constrained programming methods through direct incorporation of fuzziness and randomness within a general optimization framework; (2) it will not lead to more complicated intermediate models and thus have lower computational requirements; (3) its solutions offer flexibility in interpreting the results and reflect the interactional effects of uncertain parameters on system conditions variations; and (4) it can help examine the risk of violating system constraints and the associated consequences. The results of the case study show useful information for feasible decision schemes of agricultural activities, including the trade-offs between economic and environmental considerations. Moreover, a strong desire to acquire high agricultural income will run into the risk of potentially violating the related water quality standards, while willingness to accept low agricultural income will increase the risk of potential system failure (violating system constraints). The results suggest that the developed approach is also applicable to many practical problems where hybrid uncertainties exist.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".