Assessing Aquifer Stress Index (ASI) Using Rating Method and Analytic Hierarchy Process for a Coastal Unconfined Aquifer
Bibliographic record
Abstract
Small groundwater basins are highly vulnerable to over draft and susceptible to droughts as they are locally recharged. The sustainable development and management of groundwater basins therefore benefits from quantitative assessment of the basin status in terms of the current stress level. This paper introduces the Aquifer Stress Index (ASI) using a rating method and Analytic Hierarchy Process (AHP), a widely used multi-criteria decision support technique. Six evaluation criteria were used to determine the ASI; water levels, water quality, groundwater pumping, saline water intrusion, recharge and land use threat. For each criterion, a rating score and weight are used to evaluate the stress level. Rating scores for criteria were assigned based on multiple datasets obtained from the field investigations. Weightings for criteria were determined by pairwise comparison of AHP process. Based on the ASI, five characteristic stress regimes of the aquifers are defined: no stress, low stress, moderate stress, high stress and extreme stress. The stress level indicates the extent of groundwater availability and current development impact on the aquifer integrity. The method was applied in detail to Uley South coastal aquifer, and results indicate that the overall stress level of the aquifer is moderate. This research indicates that declining water levels are the major cause of Uley South basin’s aquifer stress, due to ongoing extractions and reduced long-term recharge. Depending on the aquifer stress level, management plans can be developed for sustainable use of the aquifer to help ensure current and future water security.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.001 | 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".