Suitability of water quality index methods for assessing groundwater quality in the Ganges River basin area
Bibliographic record
Abstract
This study considered three different water quality indices (WQIs) for groundwater data collected from the middle-west part of Bangladesh, and the quantitative results were justified with the globally accepted water quality guidelines. It compared the results between the Canadian WQI with the Weighted Average WQI, and the Canadian WQI and Mierels WQI for drinking and irrigation purposes, respectively. The results revealed that the Canadian method categorized water as ‘fair’ quality while the Weighted WQI model results showed ‘unsuitable’ for drinking usage. Besides, the Meireles method showed that the water quality is classified as ‘good’ to ‘excellent’, while the categorization of the groundwater using the Canadian method was ‘fair’ to ‘good’ for the suitability of irrigation. When comparing the results of the Canadian method with the Weighted Average technique for drinking, the latter one gave the abnormal results; and pair difference statistics showed the significant negative correlation (r=−0.91) between them. Similarly, the analysis of the two methods (Canadian and Mierels) for irrigation use exhibited that there was no statistical variance between the two techniques at a significant correlation matrix (r=+0.71). The study concluded that the Canadian WQI for drinking and Mierels WQI for irrigation would deliver better results. HIGHLIGHTS The methods used in this study clearly illustrated the conditions for groundwater to be used for drinking and irrigation purposes.; Three water quality indices (WQIs) were used and discussed the guidelines to improve the efficacy.; The findings of this study can be used to develop and improve the quality of water that directly affects public health and crop production.;
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".