Robust prediction of water arsenic levels downstream of gold mines affected by acid mine drainage using hybrid ensemble machine learning and soft computing
Bibliographic record
Abstract
Water pollution from hazardous materials, particularly arsenic, downstream of gold mines poses severe environmental and health risks. This study employs a systematic approach to predict water arsenic (WA) levels downstream of gold mines affected by acid mine drainage. WA data from the affected region were collected and preprocessed to standardize the dataset and mitigate overfitting risks. Advanced ensemble machine learning methods, particularly Light Gradient Boosting Machine (LightGBM), with two models developed: a manually-adjusted version and an optimization-based model using Jellyfish Search Optimizer (JSO). The performance of the LightGBM-JSO model was evaluated against a range of ensemble learning models, metaheuristic algorithms, and artificial intelligence techniques. Models were evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R 2 ), root mean square error (RMSE), weighted mean absolute percentage error (WMAPE), mean relative error (MRE), scattered index (SI), ρ, and the Final Rating (FRa) methodology. The LightGBM-JSO outperformed other models, achieving a training phase MAE of 148.763, MAPE of 62.081, R 2 of 0.996, RMSE of 183.692, WMAPE of 0.08, SI of 0.097, ρ of 0.048, and MRE of − 0.379. In the testing phase, it had an MAE of 19.496, MAPE of 10.686, R 2 of 0.990, RMSE of 37.386, WMAPE of 0.136, SI of 0.241, ρ of 0.121, and MRE of 0.03. Uncertainty analysis confirmed the model's reliability with a prediction interval of ± 0.05 mg/L for arsenic concentration. This study provides evidence to support environmental management decisions, providing valuable insights for regulatory bodies, policymakers, and stakeholders to support sustainable mining practices. • Arsenic levels downstream of gold mines were predicted using advanced machine learning. • LightGBM-JSO outperformed other models in predicting arsenic contamination. • Comprehensive model evaluation confirmed high accuracy and reliability. • Study supports sustainable mining through evidence-based environmental management.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".