Flood Management of Sistan River using Levee
Bibliographic record
Abstract
Flood is one of the natural disasters that leave great deal of financial and human losses every year. Application of levee is among the common flood control measures. Sistan Alluvial Plain is permanently prone to floods because of being situated on the edge of Hirmand Basin and also having negligible ground slope. Increasing of maximal flow rate by levee, using HEC-RAS mathematical model were studied in the present research. For this purpose, the geometrical plan of the river was prepared with the aid of HECGeoRAS software and through using regional topography data. After importing the data to HEC-RAS model, flood phenomenon was simulated in different return periods. Results of the current research indicate that Sistan River is not able to transport water at a flow rate exceeding 810 (m 3 /s); this current is equivalent of flood with recurrence interval of 9 years. To enhance the flood conveyance capacity of Sistan River, levee structure was applied. The results show that using levee increased river’s conveyance capacity up to the flow rate of 1700 (m 3 /s) which is equivalent of a recurrence interval of 107 years. Therefore, application of levee on Sistan River can favorably mitigate the risks resulting from flooding in Sistan Plain.
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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 | 0.000 |
| 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) | 0.001 | 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".