Flood inundation mapping along a downstream river segment
Bibliographic record
Abstract
According to the IPCC, Eastern Canada is an area where heavy precipitation events are likely to intensify. The Humber River basin is a medium-sized basin located in the Greater Toronto Area, in Southern Ontario, Canada, which is exposed to severe storms resulting in flash floods. A severe storm that passed by the city of Toronto on July 8, 2013 caused a flood with damages across the area, including blackouts and citizens trapped in public transportation and vehicles. Hydro-meteorological stations close to the basin’s outlet, in the urban section, recorded 60-63 mm of rain in two-three hours. The analysis of the examined river segment, including several bridge structures, is performed with two hydraulic models (1D and 2D) by using a high-resolution DTM and two flow hydrographs as input boundary conditions. The 2D hydraulic model provides more detailed results regarding the maximum flood depths, flood wave velocities, and arrival times of maximum depths, at every grid cell of the computational mesh. In comparison, the 1D model provides results at cross-sectional level, and interpolates them in the intermediate positions. The differences between the two models in low-height bridge locations are considerable. The 2D model can be improved by enforcing grid cells at bridges’ locations. However, there is a risk of possible instabilities in solving the shallow water equations by assuming a Courant number kept in low levels. Moreover, during storm events, water level gauges in situ measurements can improve calibrating both hydraulic models. The probable increase in precipitation heights due to climate change indicates the necessity for effective flood risk management in the urban area of the city of Toronto. On-going research concerns the effect of projected extreme precipitation on peak runoff and downstream flood impacts via climate model datasets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".