Ice‐jam flood risk assessment and mapping
Bibliographic record
Abstract
Abstract In northern regions, river ice‐ jam flooding can be more severe than open‐water flooding causing property and infrastructure damages, loss of human life and adverse impacts on aquatic ecosystems. Very little has been performed to assess the risk induced by ice‐related floods because most risk assessments are limited to open‐water floods. The specific objective of this study is to incorporate ice‐jam numerical modelling tools (e.g. RIVICE, Monte‐Carlo simulation) into flood hazard and risk assessment along the Peace River at the Town of Peace River (TPR) in Alberta, Canada. Adequate historical data for different ice‐jam and open‐water flooding events were available for this study site and were useful in developing ice‐affected stage‐frequency curves. These curves were then applied to calibrate a numerical hydraulic model, which simulated different ice jams and flood scenarios along the Peace River at the TPR. A Monte‐Carlo analysis was then carried out to acquire an ensemble of water level profiles to determine the 1 : 100‐year and 1 : 200‐year annual exceedance probability flood stages for the TPR. These flood stages were then used to map flood hazard and vulnerability of the TPR. Finally, the flood risk for a 200‐year return period was calculated to be an average of $32/m2/a ($/m2/a corresponds to a unit of annual expected damages or risk). Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".