Applying the HEC-RAS model and GIS techniques in river network floodplain delineation
Bibliographic record
Abstract
A direct-processing approach to river system floodplain delineation is developed. Floodplain zones of part of the South Nation River system, located just east of Ottawa, Ontario, are mapped in two dimensions and three dimensions by integrating the hydraulic model of the choice with geographic information systems (GIS). The first objective was to construct and validate a Hydrologic Engineering Center's River Analysis System (HEC-RAS) river network model of the system using existing HEC-2 model-generated data. Next, HEC-RAS simulations were performed to generate water surface profiles throughout the system for six different design storm events. The in-channel spatial data of HEC-RAS were then geo-referenced and mapped in the GIS domain and integrated with digital elevation model (DEM) over-bank data to build a triangular irregular network (TIN) terrain model. In the final step, floodplain zones for the six design storms were reproduced in three dimensions by overlaying the integrated terrain model for the region with the corresponding water surface TIN.Key words: river, floodplain, delineation, GIS-approach, HEC-2 model, HEC-RAS model, data query.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".