Practical Issues in Floodplain Mapping Over Large Regions
Bibliographic record
Abstract
Flooding events are among the costliest and most frequent natural hazards occurring in Canada. Floodplain mapping is a non-structural flood management strategy that involves the formulation of hydrologic and hydraulic models to produce maps which predict extent and depth of floods. Practices and availability of floodplain mapping vary across Canada. The current state of floodplain mapping across Canada has been identified and reviewed. Vast areas of flood prone regions across Canada have been identified as not having floodplain maps or lacking updated ones. Large region floodplain maps have been recently introduced and can cover national and global regions. Limitations of spatial resolution exist in large region mapping efforts, which hinder their implementation for local scale floodplain management practices. A recent study at Western University produced a national floodplain map with a spatial resolution of 1 km x 1 km. This national floodplain map is highly accurate; however, spatial resolution needs to be improved to be implemented within local scale floodplain studies. The study presented in this thesis developed a downscaling methodology to further improve spatial resolution of the floodplain map. The downscaling methodology was implemented to produce floodplain maps at spatial resolutions of 20m, 40m, 60m, 80m, 100m, 200m, 300m, and 400m for two case study river basins: Bow and Elbow River Basin and St John River Basin. Analysis of the floodplain maps was completed, followed by volume conservation and computational time studies to assess the accuracy of the proposed downscaling methodology and to compare the sensitivity of the downscaling methodology.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".