Comparative Analysis of Traditional and Ensemble Models for Water Quality Index Prediction with Explainable AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate prediction of water quality is vital for effective environmental management. This study presents a comparative analysis of traditional and ensemble machine-learning models for predicting the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) using EPA Ireland coastal monitoring data. A standardized and leakage-proof pipeline was employed with robust scaling and multiple cross-validation across multiple random seeds to ensure stable and reproducible performance. Among all models, XGBoost achieved the best performance (R2 = 0.991). Model interpretability was enabled by SHAP analysis supported by feature correlation that identified Dissolved Oxygen as the dominant factor of WQI. Overall, results illustrate the potential of ensemble learners combined with explainable AI in making accurate, interpretable, and generalizable water-quality predictions to enable data-driven environmental monitoring and decision-making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it