GIS Derived Synthetic Rating Curves and HAND Model to Support On-The-Fly Flood Mapping
Bibliographic record
Abstract
This thesis examines how synthetic rating curves (SRCs) might support the incorporation of the Height Above Nearest Drainage (HAND) model into an On-The-Fly flood mapping web-application with a Canada-wide service. To create SRCs across Canada, a custom ArcGIS Pro tool called the Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D) was developed and designed to work with publicly available data. Because SRC accuracy is sensitive to roughness coefficient (n) values, three methods (single, weighted, and minimum-median) of representing multiple surface roughness types were experimented with. The tool was tested with control data from gauge stations across Central and Eastern Canadian study sites to analyse the effects of river length, river gradient, and the n methods on tool performance. The results of these tests indicated that CERC-HAND-D produces SRCs with higher accuracy (NRMSE = 3.7% - 8.8%) in areas with river gradients above 0.002 m/m and river lengths under 5-km, while none of the n methods notably increased tool performance. An additional test on flood mapping accuracy using both CERC-HAND-D and the HAND model, through recreating the 2011 Richelieu flood in Quebec, resulted in high scores for classification evaluation (MCC = 0.776 - 0.877). Based on the findings of this thesis, both CERC-HAND-D and the HAND model were applied to the development of a prototype for an On-The-Fly flood mapping web application.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".