A dynamic DRASTIC-based approach for multi-hazard groundwater vulnerability mapping
Bibliographic record
Abstract
This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index (MHGI) to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity. Incorporating factors such as population growth, agricultural practices, and groundwater extraction enhances the framework’s ability to capture multi-dimensional, spatiotemporal changes in groundwater vulnerability. Additional improvements include refined weighting and rating scales for thematic layers based on available observational data, and the inclusion of distributed recharge. We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin, a major groundwater-dependent agricultural area in South Asia. Results indicate that between 2005 and 2020, 54% of the study area became highly vulnerable to pollution. The MHGI revealed a 13% decline in potential groundwater storage and a 25% increase in groundwater-stressed zones, driven primarily by population growth and intensive agriculture. Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19% decline in areas of low to very low vulnerability and a 6% reduction in medium vulnerability zones by 2020. Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge (42%) and renewable groundwater stress (38%). Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI. The framework provides valuable insights to guide sustainable groundwater management, safeguarding both environmental integrity and human well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".