Water Quality Index: A Fuzzy River-Pollution Decision Support Expert System
Bibliographic record
Abstract
Water quality management policies, which are proposed to prevent, control, or treat environmental problems related to quality of water, are broad and complex issues. We have various types of water resources, different water uses, and a lot of decision parameters with several levels of decision makers involved. Moreover, there are a lot of strategies and technologies available to be applied for water quality management and so environmental decision makers are required to evaluate and prioritize them in order to choose the best possible plan for each particular problem. To provide a comprehensive but easy to use tool in the assessment and evaluation of water quality policies, the concept of water quality index (WQI) has been developed. Due to the abovementioned complexities, to get this index, there is a need for a methodology to not only structure and identify information relevant to the problem but also to help users reach a decision. Designing a multiple-attribute decision support expert system, which makes expert knowledge available to nonexpert users, can do this. In doing so, we may encounter qualitative or linguistic assessments in the index making process. Thus, fuzzy set theory can be applied to recognize this inherent fuzziness of such a process. Briefly, in this study we propose a fuzzy multiple-attribute decision support expert system to compute the water quality index and to provide an outline for the prioritization of alternative plans based on the amount of improvements in WQI. At the end, applicability and usefulness of the proposed methodology is revealed by a case study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".