Topography- and nightlight-based national flood risk assessment in Canada
Bibliographic record
Abstract
Abstract. In Canada, flood analysis and water resource management, in general, are tasks conducted at the provincial level; therefore, unified national-scale approaches to water-related problems are uncommon. In this study, a national-scale flood risk assessment approach is proposed and developed. The study focuses on using global and national datasets available with various resolutions to create flood risk maps. First, a flood hazard map of Canada is developed using topography-based parameters derived from digital elevation models, namely, elevation above nearest drainage (EAND) and distance from nearest drainage (DFND). This flood hazard mapping method is tested on a smaller area around the city of Calgary, Alberta, against a flood inundation map produced by the city using hydraulic modelling. Second, a flood exposure map of Canada is developed using a land-use map and the satellite-based nightlight luminosity data as two exposure parameters. Third, an economic flood risk map is produced, and subsequently overlaid with population density information to produce a socioeconomic flood risk map for Canada. All three maps of hazard, exposure, and risk are classified into five classes, ranging from very low to severe. A simple way to include flood protection measures in hazard estimation is also demonstrated using the example of the city of Winnipeg, Manitoba. This could be done for the entire country if information on flood protection across Canada were available. The evaluation of the flood hazard map shows that the topography-based method adopted in this study is both practical and reliable for large-scale analysis. Sensitivity analysis regarding the resolution of the digital elevation model is needed to identify the resolution that is fine enough for reliable hazard mapping, but coarse enough for computational tractability. The nightlight data are found to be useful for exposure and risk mapping in Canada; however, uncertainty analysis should be conducted to investigate the effect of the overglow phenomenon on flood risk mapping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".