Distribution-Informed Machine Learning for Flash Flood Susceptibility: Integrating Weibull Extreme Value Theory with Interpretable Models
Bibliographic record
Abstract
Flash floods represent one of the deadliest weather-related hazards globally, yet their prediction remains fundamentally challenged by extreme class imbalance in observational data. This study addresses a critical methodological gap: traditional evaluation metrics, both overall accuracy and Area Under the ROC Curve (AUC), are systematically misleading for rare event prediction. We demonstrate empirically how models achieving 93% accuracy and AUC exceeding 0.98 can simultaneously fail to detect 65% of flood events. Moving beyond conventional approaches, we introduce distribution theory-informed feature generation by integrating Extreme Value Theory through Weibull distribution analysis. We derive 24 features from rigorous statistical characterization of precipitation extremes spanning 16 years (2010-2026) of ERA5-Land reanalysis over Nova Scotia, Canada. Evaluating seven model configurations using Environment and Climate Change Canada operational warning thresholds, we find that adding just six Weibull-derived features to a Random Forest baseline nearly doubles flood detection, with recall increasing from 0.35 to 0.65 and F1-score from 0.48 to 0.74, while maintaining 87% precision. This controlled comparison provides the clearest evidence for the value of distribution-informed features. Across architectures, Support Vector Machines with selected features achieve 93.4% balanced accuracy with perfect recall, while Artificial Neural Networks achieve a balanced operational profile (75% recall, 65% precision). SHAP analysis reveals that physically meaningful interaction features, particularly the intensity-duration product and rain-on-saturated-soil, dominate predictions, with raw precipitation ranking only sixth, confirming that models learn genuine multivariate susceptibility structure rather than recovering classification thresholds. These findings provide essential guidance for practitioners: comprehensive reporting of balanced accuracy, precision, and recall is mandatory for imbalanced datasets where traditional metrics mask operational failure.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".