Use of image velocimetry techniques on citizen videos of the November 2021 flooding event flows in Merritt, British Columbia
Bibliographic record
Abstract
In November 2021, an atmospheric river swept the Pacific Northwest region, causing one of the costliest natural disasters in Canadian history. Among others, the Coldwater River in Merritt, British Columbia caused widespread flooding on November 15th, 2021, resulting in extensive damage to the infrastructure and total evacuation of the residents.Estimating the magnitude of this flood is difficult, as it damaged the local flow monitoring station and altered the surrounding landscape. However, parts of this flooding event, including the flow close to its peak, were filmed by local residents using mobile devices or drones. Though with significant perspective distortion and imprecision, they still provide valuable information on the extreme flow event, which would have otherwise been lost or neglected. The objective of this study is to apply image velocimetry techniques to these videos, with limited resources and geodata, for reconstructing surface velocities and discharges during the flood.The analysis method consists of using LSPIV and Farneback optical flow on the original clips where possible. Objects are identified in the videos, then geolocated or surveyed after the flood, for rectification of raw velocities. This allows multiple iterations, accounting for uncertainties in the rectification parameters. Discharges are then calculated using surveyed or reconstructed transects, and water surface elevations estimated from the video frames.Preliminary results of both methods will be presented and compared on the use of lens distortion correction, different contrast enhancement block sizes, and interrogation area or filter sizes. Validations of the calculated discharges against flow observations from the Water Survey of Canada will also be included.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".