Flood hazard mapping techniques with LiDAR in the absence of river bathymetry data
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In many areas of the world, flood risk assessment is either out of date or completely lacking.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".