Simulation of 2D Depth Averaged Saint Venant Model of Shatt Al Arab River South of Iraq
Bibliographic record
Abstract
Shatt Al Arab River in Basrah province southern Iraq is a tidal stream and it represents the main feeder of water to Basrah province.The river is characterized by having many branches during its course from upstream to downstream.The main aim of this study is to model a 2D hydrodynamic model for Shatt Al-Arab River with seven branches inside Basrah city which are Jubyla, Muftya, Robat, Khandek, Ashar, Al-Khora, and Saraji in addition to Karmat Ali river by the Hydrologic Engineering Centre's River Analysis System (HEC-RAS) software, where most of the previous studies relied on the consideration of the river as one-dimensional, i.e., neglecting the transverse or vertical directions.Accurate input data like Digital Elevation Models (DEMs) were provided and enhanced with the help of Geographic Information Systems (GIS) as well as a data for the year 2014 was used as boundary conditions to develop the hydrodynamic model.The discharge values at the Qurnah station were extracted from the results of a previous one-dimensional HEC-RAS mathematical model.To prove the efficiency of the model, mean absolute error (MAE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency (NSE) were implemented to check the convergence between observed and simulated data through calibration and validation processes.The result of NSE was 0.836, and this indicates an acceptable convergence between the simulated and observed values.Also, the methods of RMSE and MAE support that convergence to zero.It is worth noting here that the NSE method was more sensitive to the change in the resulting values and therefore it can be recommended to use this method in hydrodynamic models.The hydrodynamic model can provide important information for models of sediment transport or water quality.Therefore, it is essential to have a good understanding of the hydrodynamic processes in a water system, before embarking on studies of sediment transport, or water quality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".