A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems
Bibliographic record
Abstract
Abstract This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".