Enhancing Coastal Flood Resiliency in Canada Through Hazard and Life Safety Assessments
Bibliographic record
Abstract
Home to the world’s longest coastline, Canada has experienced devastating economic and social from coastal flooding events. While there have been a variety of mitigation methods employed over the years to increase a community’s resistance to coastal hazards, it is unrealistic to think that there exists a solution to guarantee a community’s safety under all possible flood hazards. Instead, the community’s efforts to raise their resistance to flood hazards should be augmented with careful planning and management to increase a community’s resilience to flood hazards, allowing them to recover quickly after a natural disaster. The first step in elevating a community’s resilience is to better understand the expected hazards that it may experience. This thesis presents two unique case studies to better understand the flooding hazards present on the Canadian coastline. A large-scale numerical model that accounts for the presence of ice was developed to investigate storm surges in Canada’s western Arctic. It was found that the quality of the climatic forcing data used, ERA5, was poor in capturing peak wind speeds, but could be compensated for by using elevated wind drag coefficients. The use of non-traditional high-water marks such as driftwood lines were validated and were shown to significantly alter expected flood return periods compared to the return periods estimated from only the incomplete tide gauge measurements present on the Arctic coastline. The second case study extends the results of a tsunami hydrodynamic simulation on Canada’s Pacific coastline through a life safety assessment. The performance between an agent-based and GIS-based approach to modelling tsunami evacuation were directly compared and were shown to yield different magnitudes in fatality rate and facility demand, but similar trends. Both models agreed on a mitigation option that can significantly reduce the loss of life during a tsunami.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".