The application of data-driven modelling for the water quality index: a case study in Canada
Bibliographic record
Abstract
ABSTRACT The water quality index (WQI) is widely used to assess the overall quality of water resources using numerical values. It is a critical tool for both decision-makers and the public to understand the status of water quality. Many WQIs can be found in the literature under different jurisdictions. However, no site-specific index can be found in Saskatchewan. The current research explores the application of data-driven methods for WQIs in the North Saskatchewan River. In total, 444 samples were analyzed using 8 key water quality parameters, over 5 river cross-sections from 2012 to 2022. The National Sanitation Foundation (NSF) index was used as a benchmark. The dissolved oxygen (DO), pH, temperature (T), and turbidity (Tr) were identified as pivotal parameters, through correlation-based feature selection, to reduce input dimensionality and improve model efficiency. Five algorithms were applied, namely M5, particle swarm optimization (PSO), differential evolution (DE), gene expression programming (GEP), and multivariate adaptive regression splines (MARS). Sensitivity analysis was conducted to highlight the influence of DO and pH using M5 and GEP models. The findings underscore the potential of data-driven methods to simplify WQIs, offering a practical tool for informed decision-making.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".