Prediction and Analysis of Coastal Water Quality Using Ensemble Machine Learning Classifiers Based on Water Quality Index (WQI) Assessment
Bibliographic record
Abstract
Access to clean, safe water is vital for both environmental sustainability and human health. Water quality assessment and management can benefit greatly from the use of predictive models, which is made possible by the development of sophisticated technology such as machine learning. This study makes use of a dataset that includes a variety of metrics that were gathered from several water sources, including turbidity, dissolved oxygen, pH levels, and other contaminants. In order to deal with missing values, outliers, and normalization, data preparation techniques are first used. The most pertinent variables influencing water quality are then found using feature selection techniques. The effectiveness of a number of well-known ML classifiers, such as Random Forest, Support Vector Machines, Decision Trees, and XGBoost, in predicting water quality is assessed and contrasted. Cross-validation techniques are used to train, validate, and test the models in order to guarantee their generalizability and robustness. The experimental outcomes show how well the suggested method works to forecast the levels of water quality. In particular, the XGBoost performs better with low overfitting and great precision. Furthermore, feature importance analysis identifies important variables offering environmentalists and policymakers insightful information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".