Modeling the risk of groundwater contamination using modified DRASTIC and GIS in Amman-Zerqa Basin, Jordan
Bibliographic record
Abstract
Abstract Amman-Zerqa Basin (AZB) is the second largest groundwater basin in Jordan with the highest abstraction rate, where more than 28% of total abstractions in Jordan come from this basin. In view of the extensive reliance on this basin, contamination of AZB groundwater became an alarming issue. This paper develops a Modified DRASTIC model by combining the generic DRASTIC model with land use activities and lineament density for the study area with a new model map that evaluates pollution potential of groundwater resources in AZB to various types of pollution. It involves the comparison of modified DRASTIC model that integrates nitrate loading along with other DRASTIC parameters. In addition, parameters to account for differences in land use and lineaments density were added to the DRASTIC model to reflect their influences on groundwater pollution potential. The DRASTIC model showed only 0.08% (3 km2) of the AZB is situated in the high vulnerability area and about 30% of the basin is located in the moderately vulnerable zone (mainly in central basin). After modifying the DRASTIC to account for lineament density, about 87% of the area was classified as having low pollution potential and no vulnerability class accounts for about 5.01% of the AZB area. The moderately susceptible zone covers 7.83% of the basin’s total area and the high vulnerability area constitutes 0.13%. The vulnerability map based on land use revealed that about 71% of the study area has low pollution potential and no vulnerability area accounts for about 0.55%, whereas moderate pollution potential zone covers an area of 28.35% and the high vulnerability class constitutes 0.11% of AZB. The final DRASTIC model which combined all DRASTIC models shows that slightly more than 89% of the study area falls under low pollution risk and about 6% is considered areas with no vulnerability. The moderate pollution risk potential covers an area of about 4% of AZB and the high vulnerability class constitutes 0.21% of the basin. The results also showed that an area of about 1761 km2 of bare soils is of low vulnerability, whereas about 28 km2 is moderately vulnerable. For agriculture and the urban sector, approximately 1472 km2 are located within the low vulnerability zone and about 144 km2 are moderately vulnerable, which together account for about 8% of the total agriculture and urban area. These areas are contaminated with human activities, particularly from the agriculture. Management of land use must be considered when changing human or agricultural activity patterns in the study area, to reduce groundwater vulnerability in the basin. The results also showed that the wells with the highest nitrate levels (81–107 mg/l) were located in high vulnerable areas and are attributed to leakage from old sewage water.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".