A new methodology for flood hazard assessment considering dike breaches
Bibliographic record
Abstract
This study focuses on development and application of a new modeling approach for a comprehensive flood hazard assessment along protected river reaches considering dike failures. The proposed Inundation Hazard Assessment Model (IHAM) represents a hybrid probabilistic‐deterministic model. It comprises three models that are coupled in a dynamic way: (1) 1D unsteady hydrodynamic model for river channel and floodplain between dikes; (2) probabilistic dike breach model which determines possible dike breach locations, breach widths and breach outflow discharges; and (3) 2D raster‐based inundation model for the dike‐protected floodplain areas. Due to the unsteady nature of the 1D and 2D models and runtime coupling, the interdependence between the hydraulic loads on dikes at various locations along the reach is explicitly considered. This ensures a more realistic representation of the fluvial system dynamics under extreme conditions compared to the steady approaches. The probabilistic dike breach model describes dike failures due to three failure mechanisms: overtopping, piping and slope instability caused by seepage flow through the dike core (micro‐instability). The 2D storage cell model computes various flood intensity indicators such as water depth, flow velocity, and inundation duration. IHAM is embedded in a Monte Carlo simulation in order to account for the natural variability of the input hydrograph form and the randomness of dike failures. Besides binary (wet/dry) inundation patterns, IHAM generates new probabilistic flood hazard maps for each intensity indicator and the associated uncertainty bounds. Furthermore, the novel probabilistic dike hazard maps indicate the failure probability of dikes for each considered breach mechanism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".