Estimating flood exceedance probabilities for several levee breach scenarios for an urban riverine environment in Toronto, Canada
Bibliographic record
Abstract
Assessing flood risk requires the combination of flood hazard, exposure, and vulnerability. Hence, flood hazard is a key component of flood risk assessments. As flood propagation is impacted by hydraulic structures built along the river, flood defense such as levees have gained attention as they are rarely included in large scaled flood risk assessments. However, flood events such as hurricane Katrina showcased the impact that levee failure has on flow depth, velocity, and flood extent. Therefore, its consideration should be regularly implemented in flood risk assessments. However, with current flood risk assessment methods, considering different levee failure scenarios results in numerous flood scenarios, simulations, and hazard maps. The multiplication of simulations and maps increases the complexity of flood risk management. We propose to improve flood hazard assessments by considering a single probabilistic flood map accounting for several flood events and levee breaching scenarios. For flood events enabling the performance assessment of the levee (i.e. levee breaching), we assessed levee failure probabilities, associating each levee segment to a fragility curve. Then, we defined breaching and non-breaching scenarios and ran flood simulations using HEC-RAS and its integrated parametric levee breaching model. We propose a new method to compute flood scenario probabilities and flood exceedance probabilities. The cumulative flood exceedance probability provides a curve for every location of the flooded area. Using GIS, we applied this method to the entire flooded area, resulting in an interactive flood hazard map. An application to the Etobicoke Creek located in the Greater Toronto Area showed that this new approach provides an operational levee breaching flood hazard method that can be used in integrated flood risk assessments.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".