Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers
Bibliographic record
Abstract
This study presents the first comprehensive evaluation of groundwater quality in Siwa Oasis, Egypt, integrating advanced machine learning and statistical approaches to assess contamination, health risks, and industrial suitability. Thirty samples from the Nubian Sandstone Aquifer (NSAS) and karst springs were analyzed using Self-Organizing Maps (SOM), Principal Component Analysis (PCA), and Canadian Water Quality Index (CCME WQI). SOM clustering revealed three distinct water types: (1) hypersaline springs (TDS >10,000 mg/L) near Siwa Lake, (2) moderately saline springs (4,551–8,885 mg/L), and (3) freshwater NSAS samples (<1,000 mg/L). PCA identified salinity (45.5% variance), carbonate equilibrium (21.3%), and anthropogenic inputs (11.5%) as dominant controls. The CCME WQI classified 28% of samples as "Poor/Marginal," with localized heavy metal (Ba, V) contamination confirmed by MPI and NCI indices. Monte Carlo-based health risk assessment revealed severe non-carcinogenic risks for children (HI >1), primarily from Co (HQ up to 105.5) and V (HQ up to 416.9) via ingestion. Industrial indices (LSI, RSI, CSMR) highlighted scaling potential in freshwater zones (LSI >1.5) and corrosion risks in saline areas (RSI >8). As the first study to: (1) quantify emerging contaminants (V, Co, Mo) in NSAS, (2) apply SOM-PCA-Monte Carlo integration in arid aquifers, and (3) concurrently evaluate health and industrial risks, this work provides a replicable framework for non-renewable aquifer management. Immediate actions targeted remediation, infrastructure protection, and agricultural regulation are recommended in Siwa Oasis. The methodologies and gaps identified including unassessed carcinogenic metals and isotopic tracing set a roadmap for future research in vulnerable aquifer systems.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".