Flood Preparedness Application Using Pre-determined Global Flood Inundation Maps
Bibliographic record
Abstract
In Canada, floods are the most common and most costly natural disaster. Floods threaten lives, properties, and the environment and these risks are only expected to increase alongside expected population increase and impacts from climate change. Flood early warning systems (FEWs) can help mitigate the impact of floods by helping inform the public when and where a flood may occur, identifying infrastructure that may be impacted, and disseminating evacuation routes that avoid flooded roads. FEWs have been shown to save lives and mitigate flood impacts. However, many existing FEWs are limited in terms of their forecast horizon and geographical coverage, and also require precise hydraulic models and substantial computing.This paper develops a flood preparedness application for all of Canada to help prepare Canadians for future and imminent floods. This Canadian flood preparedness application addresses limitations associated with many of the developed FEWS in Canada by matching predicted river flows to predetermined return periods for developed global (or country-wide) flood inundation maps. By matching predicted river flow to return periods of predetermined inundation maps, complex computation is avoided reducing response time, and improving geographical coverage (by using a Canada-wide model). Lastly, using the static map approach, the public and emergency personal can help prepare for floods well in advance, identifying their own flood risk and as well as evacuation and muster locations strategies by identifying roads that would likely be flooded under various flood return periods. Overall the Canada-wide flood preparedness application will help protect and better prepare Canadians as flood risks continue to rise by increasing forecast horizon and geographical coverage and minimizing computation. The new approach of using global (or country-wide) static flood inundation maps to inform FEWS may be applicable in other countries where detailed hydraulic models are unavailable or too time consuming to calculate on a continuous or as needed basis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".