Developing Spatial Models of Groundwater Quality in the Southwestern Desert of Iraq Using GIS, Inverse Distance Weighting, and Kriging Interpolation Techniques
Bibliographic record
Abstract
Water scarcity is a prevalent issue in Iraq, and groundwater resources are critical for addressing this problem, particularly in the country's desert regions. This study aimed to assess groundwater quality and develop spatial models using geographic information systems (GIS), inverse distance weighting (IDW), and Kriging interpolation techniques in the southwestern desert of Iraq. Water samples were collected from 75 wells, spanning an area of 50,488 km , and were analyzed during both the dry and wet seasons. The water quality characterization included measurements of electrical conductivity (EC), total dissolved solids (TDS), pH, and major cations and anions in the groundwater. Results indicated a high range of TDS values, corresponding to elevated EC levels, with pH values ranging from 7.1 to 8.3 across the study area, as revealed by the GIS models. It was found that the concentrations of major cations (Ca and Mg) and anions (HCO3, Cl, and SO4) exceeded the acceptable limits for drinking water set by the World Health Organization (WHO) and Iraqi drinking water specifications, with noticeable variations in the distribution of these elements within the study area. Furthermore, seasonal fluctuations were observed to have a significant impact on the groundwater quality characteristics. In conclusion, a wide range of water quality characteristics was identified in the study area, and the developed spatial models can serve as valuable tools for selecting appropriate treatment methods to utilize groundwater as a drinking water source.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".