Hydrological Modeling & Flood Design in Rudbar Lorestan Dam & Power Plant Project
Bibliographic record
Abstract
Accurate modeling of the drainage basin, including its spatial and temporal distribution of hydrological parameters and rainfall-runoff process, is very important in many applications. As an example, design flood estimation in hydraulic structures, which has the main role in the construction cost, is a result of rainfall-runoff simulation. The Rudbar Lorestan dam project is a part of the hydroelectric development projects complex in the Dez River basin. This project is located in a mountainous zone 200 Km away from Isfahan on the Rudbar River and 100 Km away from the south of Aligudarz. The aim of the Rudbar Dam and power plant project was to use the hydroelectric potential that is caused by the different elevations between the dam position and the power plant location. Due to the About 300 Meters difference in elevation from the dam axis to the power plant location set as one of Iran's prominent hydroelectricity projects. There are many consulting engineers in these projects, previous studies, and their main study result (flood design) shows a 17% difference from each other. due to the significant mentioned difference caused by using experimental methods and personal judgment, an effort was made in this research to simulate a large part of the Rainfall-Runoff process and model the water movement current on the basin surface with WMS software, and by taking the results of previous studies, the results of executing point of view and theory point of view had been compared. For this purpose, two internal and external representative basins were simulated, and the results were compared to evaluate the ability of the model. Simulating the main basin refuses the older studies by the difference near 20%, and confirms the newer studies by a difference of about 10%.
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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) | 0.002 | 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".