Local Scale Current and Projected Future Total Flood Hazard Mapping for Canada—Literature Review
Bibliographic record
Abstract
ABSTRACT This review, based on 231 articles, focuses on studies relevant to Canada that assess fluvial, pluvial, and coastal flood hazards at national and broader scales. It evaluates the application of remote sensing and artificial intelligence methods for flood mapping within the Canadian context. The review highlights a growing trend in large‐scale flood modeling, with increasing relevance for Canadian flood risk management. Methods for downscaling coarse‐resolution flood estimates from physically based models to finer spatial scales are particularly important for Canada's diverse hydrological regions. Global estimates of flood defense standards often rely on socio‐economic indicators, but for Canada, physical hazard factors should also be integrated. Advances in LiDAR and radar remote sensing have improved the accuracy of Canadian flood models by providing detailed topographic data. Artificial intelligence techniques show strong potential for predicting flood inundation and enhancing flood hazard mapping across Canadian landscapes.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".