USE OF THE GROUNDWATER QUALITY INDEX, MULTIVARIATE STATISTICS AND HYDROGEOCHEMISTRY FOR GROUNDWATER ASSESSMENT IN THE MALABAR VOLCANIC AREA, INDONESIA
Bibliographic record
Abstract
The South Bandung basin has had significant population growth in the last ten years, particularly in the regions that border West Java. Consequently, there was an increase in the demand for groundwater, an essential resource for numerous uses. On the other hand, human activities have given impact significantly on the change of groundwater quality in the Bandung basin, the Malabar volcanic area. In the Bandung basin, the Malabar volcanic area has become an important location for urban water supply recharge. Within the current investigation 27 water samples were collected during the dry and wet seasons. The purpose of this study was to analyze the seasonal variability of parameters using different approaches. The comprehensive methods involving the application of multivariate statistics, geographical modelling, and the groundwater quality index. The spatiotemporal variability showed that the dilution effect of precipitation during the rainy season contributed to the significant seasonal variations. The hydrogeochemical facies was determined as Ca-Cl, CaMg-Cl, CaMg-HCO₃, and NaK-HCO₃. The Ground Water Quality Index (GWQI) analysis indicated that physicochemical factors influence water quality classifications from unsuitable to excellent. According to the conceptual model, the upstream area has excellent GWQI; however, the downstream area has decreased GWQI due to anthropogenic influence and the dissolution process. The results suggest that NH₄⁺-N, Fe²⁺, and Mn²⁺ have significant impact on GWQI. The novelties of this research include sensitivity analysis of each parameter to GWQI while conceptual model differentiates its findings from previous research. This conceptual model can be applied in various geographic environments to determine groundwater quality and its distribution regarding seasonal and land use changes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".