Assessing Spatiotemporal Water Quality Variations in Polluted Rivers with Uncertain Flow Variations
Bibliographic record
Abstract
Many rivers in developing countries receive high pollution loads from large cities. Such rivers also experience extreme flow variations due to overexploitation of freshwater resources, poor management practices, and climate change impacts. Consequently, levels of water quality parameters (e.g. biochemical oxygen demand, dissolved oxygen, unionized ammonia, and coliforms) considerably change with the extreme flow variations. The morphology of a river segment also influences the levels of water quality parameters by changing the residence time. For a river, a water quality index is a robust assessment tool to indicate the overall water quality for different segments (e.g. having natural freshwater, receiving single point source loads, and cumulative loads from several outfalls) along the length. The index also captures the impacts of different uncertainties on water quality due to seasonal variations in river flows, hydrodynamics, and pollution loads. Water quality data are scarce in developing countries due to the absence of planned periodic monitoring programs. Type-2 fuzzy sets, an extension of the ordinary fuzzy sets, directly model these uncertainties by providing an additional degree of freedom. Type-2 fuzzy sets improve the specific kind of interface that exists due to increasing uncertainties associated with imprecision in knowledge and vagueness in information due to limited water quality data. Using the triangular type-2 fuzzy sets approach, the index developed in the present work effectively caters for these uncertainties and has proved to be a reliable water quality assessment measure for highly polluted rivers with extreme flow variations in developing countries and elsewhere with similar conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".