Suitability maps for managed aquifer recharge: a review of multi-criteria decision analysis studies
Bibliographic record
Abstract
Suitability maps for managed aquifer recharge (MAR) sites hold a strong potential for integration into sustainable groundwater management plans. An uprising method to identify sites suitable for MAR implementation is geographic information system (GIS)-based multi-criteria decision analysis (MCDA). There are no guidelines or a common understanding on how suitability mapping should be conducted, and there is considerable variability as to what factors are assessed and how they are weighted. To increase knowledge on GIS-MCDA, a database has been built based on 63 studies applying GIS-MCDA in the context of MAR site selection. Information on the criteria, assigned weights, and methodologies has been retrieved from the documents. Statistical analysis of the database depicts the current state of art for suitability mapping methodologies as well as specific information for the different recharge methods. We further incorporated the compiled information into a web-based query tool that makes the information easily accessible and the utilization of the database more user friendly. This review as well as the created web-tool will help planners of MAR sites to engage in the MCDA in a more structured way by referring to previously conducted studies and by finding information suitable for their specific project. The application potential of suitability maps is discussed along with the shortcomings of this methodology.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".