A rapid flood risk assessment method for response operations and nonsubject‐matter‐expert community planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Flood risk planning and emergency response at community levels rely on fast access to accurate inundation models that identify geographic areas, assets, and populations that may be flooded. However, limited flood modelling resources are available to support these events and activities. We present a computationally‐efficient flood model for facilitating rapid risk analysis across a wide range of scenarios and decision support to operational, crisis action, local flood‐fight, and community planning efforts. Our flood depth regression method converts publicly‐available river stage heights to flood depths, then downscales the depths from gage locations onto high resolution National Hydrography Dataset flowlines and estimates areas and depths of flooding by subtraction of the National Elevation Dataset from modelled water surface elevations. We demonstrate proof‐of‐principle analyses for historic 2009 Red River of the North flooding in the United States, achieving comprehensive mainstem flood estimation for the length of the river and depth accuracy of 1.4 ft (0.4 m) compared to gage observations, remote sensing, and higher‐resolution hydrologic models. We also demonstrate the utility of the method to inform planning and response decisions in preparation for flooding in a companion scenario for Yerington, Nevada, and call for further research and operationalization of riverine inundation mapping techniques.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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