Modeling and Predicting Water Quality in the Euphrates River from Haditha Dam to Ramadi Barrage Using QUAL2KW
Bibliographic record
Abstract
This study aims to assess the applicability of the QUAL2KW model in simulating water quality in the Euphrates River from downstream (d/s) of Haditha Dam to upstream (u/s) of Ramadi Barrage, covering a stretch of approximately 180 kilometers.The river reach within the study area was split into 18 segments of unequal length.River hydraulic and hydrologic characteristics, weather data, and GIS information about the location and coordinates for each river segment were used as input data to run this model.Several water quality parameters were assessed in this study with their concentrations measured through field sampling and laboratory analysis.Statistical metrics such as Relative Error (RE) and Root Mean Square Error (RMSE) were employed to evaluate the model's performance, using observed and simulated concentration data during two distinct periods: calibration in April (2024) and validation in July ( 2024).The statistical results demonstrated the reliability of the model for this river, as indicated by the strong agreement between observed and simulated concentrations of water quality parameters.Therefore, once the QUAL2KW model is provided with the river's hydraulic and hydrological characteristics, along with data on pollutant discharges from wastewater treatment plants and drainage channels, it can be used by water resources management authorities to predict future concentrations of water quality parameters at any location within the study area.This can be achieved without the need for direct sampling by inputting data solely from the headwater (downstream of Haditha Dam at station (0) km, including discharge and pollutant concentrations.
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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".