Risk Assessment of Dam-Breach Flood Under Extreme Storm Events
Bibliographic record
Abstract
In recent years, as a result of increasingly intensive rainfall events, the associated water erosion and corrosion have led to the increase in breach risk of aging dams in the United States. In this study, a hydrodynamic model was used to the inundation simulation under three hypothetical extreme precipitation-induced homogeneous concrete dam-breach scenarios. All hydraulic variables, including water depth, flow velocity, and flood arriving time over separated nine cross-sections in the Catawba River, were calculated. The hypothetical simulation results illustrate that the impact of Hurricane Florence’s rainfall is far more severe over the downstream of hydraulic facilities than that of the Once-in-a-century storm rainfall event. Although Hurricane Florence’s rainfall observed in Wilmington had not historically happened near the MI Dam site, the river basin has a higher probability to be attacked by such storm rainfall if more extreme weather events would be generated under future warming conditions. Besides, the time for floodwaters to reach cross-section 6 under the Hurricane Gustav scenario is shorter than that under the Once-in-a-century rainfall scenario, making the downstream be inundated in short minutes. Since the probability can be quantitatively evaluated, it is of great worth assessing the risk of dam-break floods in coastal cities where human lives are at a vulnerable stage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".