Flood hazard mapping techniques with LiDAR in the absence of river bathymetry data
Why this work is in the frame
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Bibliographic record
Abstract
In many areas of the world, flood risk assessment is either out of date or completely lacking. In Quebec (Canada), one of the challenges to map flood risk is the very large territory combined with very few datasets on river bathymetry, which are required to run hydraulic models. The objective of this study is to assess the precision and accuracy of 2D flood hydraulic modelling exclusively based on LiDAR elevation data which do not include information on in-channel river bathymetry. Hydraulic simulations (HEC-RAS 5.0) are carried out, for discharges of 20-, 100- and 500-year recurrence intervals, using two techniques that do not require bathymetry data, either subtracting discharge of the LiDAR survey from the flood discharge or estimating flow depth from the water surface slope. These techniques are compared to a traditional approach using bed topography obtained from detailed field surveys, on two long reaches (several kilometers). Sensitivity tests were conducted to assess the impacts of the main sources of error on simulated flood levels. Results show that both techniques can be applied with limited introduction of error in the modelled flood stages, and that errors are greatly reduced if calibration data are available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it