Saltwater intrusion vulnerability assessment using AHP-GALDIT model in Kashan plain aquifer as critical aquifer in a semi-arid region
Bibliographic record
Abstract
Owing to population growth and water demand, coastal aquifers all over the world are over–pumped, resulting in serious problems such as saltwater intrusion. So, in these conditions, assessing the groundwater system’s vulnerability and finding areas with saltwater intrusion potential are vital for the better management of aquifers. In this study, AHP-GALDIT was applied to saltwater intrusion vulnerability assessment in the Kashan plain. The AHP model determines the weight of each indicator in the GALDIT model. The most important indicators of the AHP model are distance from shore/high tide, groundwater head, groundwater system hydraulic conductivity, impact of present status of saltwater intrusion, saturated media depth, and groundwater occurrence. The AHP-GALDIT distribution map indicates four different rating areas in the Kashan plain, including: more than 10, 7.5 to 5, 5 to 2.5 and less than 2.5, which denote high, average, low, and very low vulnerability, which correspond to approximately 16.16, 25.51, 21.26, and 36.05% of the entire area, respectively. The results reveal that the northeastern part of this inland coastal aquifer is currently undergoing saltwater intrusion. But, it is not clear whether the source of salinity is saltwater intrusion from the “salt lake”, upcoming processes, or other sources. This study proves that the GIS-based AHP-GALDIT model is suitable to determine vulnerable sites with high accuracy by using the set of indicators affecting the vulnerability assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".