Reducing Urban Flood Risk Through Building- and Lot-Scale Flood Mitigation Approaches: Challenges and Opportunities
Bibliographic record
Abstract
Urban flooding events are a significant driver of disaster loss, resulting in insured and uninsured losses, property damage, and negative impacts on residents and communities in Canada and internationally. The risk of flooding in urban environments is affected by watershed characteristics, environmental conditions, and the presence and condition of flood management and mitigation technologies. Several building- and lot-scale (or private-side) flood mitigation options are available to better protect properties from the risk of flooding, including backwater valves and foundation drainage systems to reduce the risks of sewer surcharge and infiltration flooding into basements, respectively. The overall success of private-side approaches to reduce the risk of flooding into buildings is reliant upon consistent installation procedures, building code interpretation and enforcement, public engagement, and maintenance. Current research into private-side approaches is presenting many opportunities and solutions for improved flood protection against water-related disasters at home. A greater understanding of the performance of private-side technologies under complex site-specific conditions can help to appoint flood prevention strategies better suited to individual home characteristics. This review paper explores the inter-related factors that affect the risk of basement flooding and explores the challenges and opportunities associated with the adoption and success of private-side flood mitigation approaches. Developing a greater understanding of basement flood vulnerability at the lot-scale will assist in identifying and prioritizing private-side strategies for homeowners to adopt and reduce the risk of flooding based on site-specific conditions affecting flood vulnerability. Continued efforts to evaluate and identify flood risk factors and the performance of private-side strategies are needed to better manage urban flooding events.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".