Inundation Mapping and Flood Frequency Analysis using HEC-RAS Hydraulic Model and EasyFit Software
Bibliographic record
Abstract
Conducting a flood frequency analysis and mapping of the inundated area in rivers are important for river flow modeling. The main purpose of this research is to estimate the peak flow, model the inundated area using HEC-RAS, and conduct an analytical hierarchy process for the upper Baro Akobo basin in Ethiopia. The inundation area and river depth for 25, 50 and 100 years are considered while contemplating several factors which contribute to flooding. The downstream of the basin has experienced numerous floods that occurred in 2006, 2007, 2010, 2011, and 2012. Flood frequency analysis with stream flow data from 1990–2009 at the Baro-Gambella gauging station was carried out to estimate the expected peak floods of the watershed. The analysis was conducted using the Gumbel, Normal, and Log Pearson Type III distribution methods. The peak floods with return periods of 25, 50, and 100 years with a minimum statistical value calculated using the Normal distribution method resulted in 1739.586 m3/s, 1820.872 m3/s, and 1893.974 m3/s, respectively. The HEC-RAS model results indicated that the flood inundation areas under different land use changes for 25-, 50-, and 100-year return periods were 446.2 km2 (annual crop cover), 404.4 km2 (built area cover), 323.3 km2 (flooded vegetation), and 93.58 km2 (forest area), respectively, whereas the inundation depth ranged from 0–2.6 m, 0–2.9 m, and 0–3.2 m depth at the upstream and downstream of the river, respectively. The outcome of this study could be used to reduce temporal and permanent flood risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".