Determination of Zones at Risk of Flooding During A Breakage of an “El Abiod Ghardaia” Dam
Bibliographic record
Abstract
Algeria, like other countries in the world, has experienced in the last decade, several natural disasters including floods and the breaking of dams, government institutions and others in the world are increasingly aware the phenomenon of dam failure that can cause catastrophic floods involving potential danger to the population.The El Abiod dam, the subject of this study, is vulnerable due to the nature of its design called " Renard effect", could cause its rupture.Oued M'Zab, the subject of this study, constitutes a potential danger because of the violence of its floods, on the one hand, and could have a negative impact on the hydraulic structures if they were to break on the other hand.In order to develop a hydraulic model that simulates the propagation of the flood wave along the sections studied, we adopted a methodology based on the coupling between ARCGIS and HECRAS using HEC-GEORAS.For this for the treatment of this phenomenon, the establishment of a hydraulic model is necessary for the realization of a numerical simulation of the wave of rupture of the Dam El Abiod (maximum peak flow) using the two HEC-RAS 1D and 2D digital models.The main objective of this study is to better explain the potential danger that the dam constitutes if it were to break and to spatialize the extent of the floods in order to map the flood zones which will be a support for the development of the M'Zab valley.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".