Hydraulic modeling of Sebou tributaries for flood prevention in the el Gharb plain - Morocco
Bibliographic record
Abstract
Flooding is one of the most unpredictable natural hazards. In Morocco, the El Gharb plain is the most affected. The Rharb basin receives between 500 and 600mm of precipitation and includes 30% of Morocco's water resources. All the factors that make the Rharb plain a vulnerable area are: climatic factors, lithology, geomorphology, the limited number of natural outlets for water drainage towards the Atlantic Ocean. The methodology adopted is based on the determination of flood zones and the hydraulic modeling of the main tributaries of the Oued Sebou and the main sanitation channels, in order to monitor the evolution of flood zones and evaluate the flow of the Oued Sebou to understand the functioning of the hydrographic network and the overflow points. According to the hydrographs established by the Gharb plain flood protection department, the maximum flow at the entrance to the city of Kenitra was estimated at 2,600 m3/s and the flow of the Oued at this level is of the order of 1,600 m3/s, which explains the overflows recorded at the level of the left bank of the Oued Sebou, the dead arm of the Oued. The results of these studies as well as the analysis of the history of the floods of the Oued Sebou, show that one to two major floods occur every 10 years and that the overflows reach upstream of the highway to the dead arm of the Oued and cover Merja-Fouarate such as the case of the flooding of the city of Kenitra in 2010.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".