Assessment of Water Quality Indices in the Iraqi Tigris River Using Remote Sensing Technique: A Comprehensive Study
Bibliographic record
Abstract
Abstract This study objects to evaluate the Water Quality Indices (WQIs) of the Tigris River in Wasit, Iraq, using the Arithmetic Weighted Water Quality Index (AW-WQI), Canadian Water Quality Index (CCME-WQI), Heavy Metal Pollution Index (HPI-WQI), National Sanitation Foundation Index (NSF-WQI), and Overall Index of Pollution (OIP-WQI). Twelve water samples were collected at different locations in the study area during the winter and spring of 2024. Each index evaluates the water quality in the study area based on specific criteria. In separate periods (winter and spring seasons of 2024), we categorized the water quality in the research region according to each indication: AW-WQI (70.517-102.611), CCME-WQI (39.763-47.1404), HPI-WQI (82.526-118.846), NSF-WQI (54.66-60.12), and OIP-WQI (1.9769-2.4686). We have created twenty-six combinations of spectral reflectance bands, reflectance values of seven bands, band ratios for the first five bands, and nine spectral indices. This study showed a significant correlation between the spectral reflectance data of Landsat-9 OLI-2 bands and the WQIs using Pearson correlation and multiple linear regression (MLR) model equations. We evaluated the performance of the MLR model for the WQIs across different seasons. The AW-WQI model showed a coefficient of determination R 2 of 84% in winter and 98% in spring. At the same time, the CCME-WQI recorded R 2 of 97% in winter and 75% in spring. The HPI-WQI received R 2 of 93% and 98% in spring. The NSF-WQIs received R 2 of 62% and 98% in spring. Finally, the OIP-WQI received R 2 of 92% and 99% in spring. These results highlight the seasonal variation in the predictive accuracy of the WQI models, with some minor differences between the experimental results and those obtained through remote sensing techniques. The WQIs showed that the water needed to be more suitable for consumption due to elevated levels beyond the permissible limit in most study area locations. Multiple sources of pollution in the region discharge hazardous waste into the river, causing WQIs to exceed permissible limits in most study areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".