Building a flood vulnerability index for urban resilience: Insights from Kelowna, British Columbia
Bibliographic record
Abstract
Frequent extreme weather events such as floods result in unprecedented casualties along with economic losses in cities. A thorough understanding of the vulnerability and potential risks influences the nature of preparation needed to make the cities resilient which enhances their ability to withstand any future flood events. Assessing flood vulnerability, therefore, is critical for any city authority to choose the right actions on adaptation and mitigation fronts in order to enhance its resilience. This stems from the need to create a localized flood vulnerability index (LOFVI) specific to cities. In this paper, we attempted to create a LOFVI accounting twenty-four physical, social, economic, and environmental vulnerability indicators (VIs) in the City of Kelowna (COK). COK experienced a number of major floods in the recent past while it is at risk of facing future similar and extreme events. LOFVI was designed at COK's neighborhood scale. The result suggests that it scores 44 %, which is understood to be a moderate vulnerability. Specifically, it scores “low” in social and environment vulnerability criteria, indexing 21 % and 39 % respectively. While physical, and economic dimensions score “moderate” with 56 %, and 50 % vulnerability indices respectively. The individual scores suggest the city needs to improve specific to the areas (VIs) notably, floodplains map, waterfront community, urban forest coverage area and flood insurance within the physical and economic dimensions. The proposed methodology is adaptive and capable of capturing the trajectory of vulnerability dynamics in any cities where flood is a recurrent threat. The vulnerability scores are going to potentially provide consolidated directives on how to keep the communities resilient against natural hazards. The proposed approach is equally adaptable for the assessment of flood vulnerability across other cities across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".