High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex
Bibliographic record
Abstract
Urban flash flooding is a serious problem in large highly populated areas such as the Dallas-Fort Worth metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to mitigating its threats and for cost effective emergency management. In this work, the prototype high resolution flash flood warning system under de velopment for DFW is described and a case study of the flash flooding event of 2014-06-24 in Fort Worth presented. The high resolution (500 m, 1 min) precipitation input comes from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band radars. The hydrologic model used is the National Weather Service Hydrology Laboratory's Distributed Hydrologic Model (HL-RDHM) operating at a 500 m resolution. The model simulation results are assessed using the flooding reports received from residents throughout the event by the City of Fort Worth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".