Application of HEC-RAS Software for Steady and Unsteady Hydraulic Simulation of Al-Musayyab Canals
Bibliographic record
Abstract
The Al-Musayyab Canal, a vital irrigation infrastructure in the Babil Governorate of Iraq, serves numerous cultivated areas.However, it has faced frequent flooding in recent years due to increased flow rates and changes in land use.This study aims to define the hydraulic characteristics of the 49.5 km-long Al-Musayyab Canal, which extends from the head regulator at Al-Musayyab City to Jabla City and includes 13 branches that distribute water to agricultural areas.To simulate both steady and unsteady flow conditions, HEC-RAS version 6.3 software was used, incorporating 175 cross-sections distributed along the canal.Calibration and verification with flow rates ranging from 25 to 45 m³ /s revealed that the optimal Manning roughness coefficient is 0.025, which minimizes the error ratio between observed and calculated water surface elevations for both steady and unsteady states.Various scenarios with gate openings ranging from quarter to fully open were simulated.The results indicated that, for the steady flow model, the water surface elevation ranged from 26.32 to 31.78 meters, and velocities ranged from 0.2 to 0.98 meters per second.For the unsteady flow condition, these values ranged from 26.58 to 33.12 meters and 0.22 to 0.89 meters per second, respectively.Risk areas were identified between stations 6750 and 24750 km, which require either cross-section training or embankment heightening to enhance the canal's discharge capacity and mitigate flooding during high flow rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".