Investigating the impact of model complexity on efficiency, efficacy & ease of use for storm surge modelling – A case study of Hurricane Helene, Florida
Bibliographic record
Abstract
It is a universally acknowledged fact that the changing climate is leading to more frequent and adverse storm induced flooding events. On September 26, Hurricane Helene (HH), a category 4 storm, made landfall near just south-west of Perry in Taylor County. HH was a fast-moving storm that resulted in both wind and flood induced damages finally leading to 230 fatalities across six states, with at least fourteen fatalities occurring in Florida and millions of dollars in property damages. Notably, HH was 13th category 4 storm in the preceding 18 months before it made landfall, underscoring the urgent need for numerical models that are both user friendly and efficient, yet capable of delivering high- fidelity results.This work investigates and compares the models based on Shallow Water Equations (SWEs), incorporating varying levels of complexity. The models were tested with different configurations, that is including/omitting different terms in the SWEs combined with the application of sub-grid methodology that allows for significant disparity between the computational grid resolution and the underlying digital elevation model (DEM) resolution. These models were applied to the coastal zone of the Steinhatchee River to simulate flood inundation extents and depths.The ease of application and the accuracy of results, as obtained from various models, provides valuable insights for policymakers. These insights can then be instrumental in the selection of models for efficient and effective integration into state-of-the-art flood warning system (FWS).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".