Advancing flood early warning systems: ensemble learning-based classifiers for urban flood forecasting
Bibliographic record
Abstract
ABSTRACT Flood early warning systems (FEWS), which rely primarily on flood forecasting models, are becoming increasingly important for mitigating damage to natural and built infrastructure. Machine learning (ML) provides flexible modelling solutions to modelling challenges such as FEWS. In this work, a flood forecasting approach is developed as a pure classification problem to directly predict flood events. The advantages of the proposed approach are demonstrated by appropriately discretising the streamflow or stage into binary states, instead treating them as continuous variables. This distinction is highlighted through a juxtaposition of regression and classification approaches, each used to generate flood alerts. The research also features a systematic cross-comparison of five ML models and three ensemble learning frameworks to provide additional insights on modelling applicability. Classification performance is shown to be primarily dependent on the base learner; extreme learning machines and support vector machines (SVMs) exhibit the best performance. SVMs are the only type of model to outperform the mean model performance across all four metrics considered, with improvements ranging from 2.9 to 24.9% above the mean. Boosted models consistently outperform the other ensembles, by 1.4 to 14.2% above the mean.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".