Framework for stochastic urban flood hazard mapping using coupled and industry-standard hydrologic and hydraulic models
Bibliographic record
Abstract
Flood hazard mapping based on deterministic models does not represent the uncertainties inherent in the methods. Tools to characterize this uncertainty using industry-standard hydrologic and hydraulic models are lacking. This research presents SWMM-RASpy, an open-access Python tool to stochastically sample and analyze flood inundation using the widely-used Storm Water Management Model (SWMM) for hydrology and the Hydrologic Engineering Center's River Analysis System (HEC-RAS) for channel hydraulics. Channel-floodplain hydraulics are represented in a two-dimensional, unsteady manner. The framework is tested in an urban watershed with stochastic sampling of flow roughness. For this watershed, it is shown that up to 4.5 % more of the watershed and approximately double the number of buildings may be subject to flooding if roughness uncertainty is considered relative to a deterministic model. Flood hazard uncertainty is represented using an entropy map for clear communication, which could be used to improve flood risk management.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".