Building a flood vulnerability index for urban resilience: Insights from Kelowna, British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it