Use of Flood, Loss, and Evacuation Models to Assess Exposure and Improve a Community Tsunami Response Plan: Vancouver Island
Bibliographic record
Abstract
Communities developing plans for response to tsunami require site-specific estimates of the hazard, elements at risk, and potential losses, and an assessment of the effectiveness of mitigations and protective actions. Citizens want to know whether they can reach safe havens in sufficient time and whether recommended safe haven locations can offer sufficient protection. This paper investigates how flood, loss, and evacuation predictive models can be used to develop baseline estimates of potential losses without mitigations in place, with the goal of helping communities assess and improve response plans. A case study is presented for the District of Ucluelet, British Columbia, which is susceptible to the Cascadia Subduction Zone (CSZ) earthquake and tsunami hazard. Approximately 58% of the community’s buildings and key elements of the critical infrastructure are in a tsunami hazard zone. Depending upon the time of day and year, between one-half and two-thirds of the resident and tourist population are at risk, and depending on the evacuation strategy, between one-fifth and one-third of the population-at-risk could be lost. These mortality rates are comparable to observed rates for a rapid-onset, high-intensity tsunami. Alternative emergency response plans are simulated and assessed for their effectiveness in terms of the potential for loss reduction and for increases in evacuation rates. The importance of self-activation and rapid protective action is confirmed, and pedestrian-based evacuation to an expanded set of proximal safe havens is recommended. Tourists form a large proportion of the population-at-risk during high season and could experience significant proportional losses. Future research is needed to assess the community’s understanding of tsunami risk and whether community preparedness has actually improved. This study also confirms the need for broader initiatives to estimate populations at risk, to conduct evacuation modeling studies, and to assess whether evacuation on foot, in vehicles, or in combination is most effective.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".