Decision Tree Algorithms in Water Quality Classification: A Comparative Study of Random Forest, XGBoost, and C5.0
Bibliographic record
Abstract
Safe drinking water is more than a convenience; public health officials often call it a cornerstone of survival. United Nations International Children’s Emergency Fund (UNICEF) reported that, shockingly, roughly two billion people still drink water that is neither clean nor tested. Pathogenic bacteria from human feces and livestock waste taint roughly 70% of available sources, creating a silent epidemic. Scientists express water quality into five levels: poor, marginal, fair, good, and excellent – named as the Water Quality Index (WQI) designed by the Canadian Council of Ministers of the Environment (CCME). This research measured the performance of three decision-tree classifiers, including Random Forest, XGBoost, and C5.0 to predict water quality. The preprocessing pipeline was thorough, involving label encoding, use of synthetic minor over-sampling technique (SMOTE) for balancing imbalanced classes, and an exploratory phase to examine outliers and irregularities within the dataset. According to the findings, Random Forest finished at an impressive test result with 98% of accuracy. XGBoost and C5.0 follows close behind at about 96%, but the latter turned out to be the fastest, edging out both XGBoost and Random Forest, making C5.0 a preferable when a time-sensitive or emergency decision is needed. In short, this research highlights the importance of modern preprocessing tools combined with machine learning algorithms in monitoring water quality.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".