Mapping the impact of levee failure on flood risks: A Toronto case study
Bibliographic record
Abstract
Urban areas are frequently built along rivers and earthen levees are commonly used to protect areas from fluvial floods. Levees are designed to protect assets from flooding, however, they deteriorate over time. Maintenance checks are required to maintain their efficacy but even in good condition, a levee structure may fail during a flood, hence flood risk assessments in fluvial areas require an investigation of levee failures, e.g. by overtopping, erosion, or sliding. In this research, we investigate the failure probability due to backward erosion of an adapted levee in the Etobicoke Creek watershed, in Toronto, Canada. The study proposes an adapted levee as the residential area is often flooded. Backward erosion is the most probable and challenging failure mechanism for our case study based on the levee shape and soil type. For this probabilistic study, the levee was modelled using GeoStudio, which produces seepage analysis from geotechnical and hydrological parameters. The seepage analysis provides hydraulic gradients from which we determine the failure probability of backward erosion based on a critical hydraulic gradient value. To obtain the flood hazard, we use a steady flow hydraulic model (HEC-RAS) to simulate the 350-years return period flow through the River. We compare two backward failure scenarios: one with a levee breach and one without, to better understand how failure of the levee will impact flood risks, and therefore, highlighting the importance of on-going levee maintenance. To obtain flood risk maps, the flood hazard (i.e., flood extent) is combined with flood exposure. The flood exposure includes land-use type (residential, commercial, etc.) and demographic information. Flood hazard and exposure data are combined using ArcGIS. The flood hazard and exposure rasters are reclassified in a new scale to determine flood risk. We then overlay the rasters to determine the spatial distribution of flood risk for both scenarios. We compare the resulting flood risk maps and calculate the change in flood risks for the area protected by the levee. Accounting for potential failure of infrastructure in flood risk mapping results in more accurate risk estimations. We also demonstrate the positive impact of the levee.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".