Machine learning-based regional flood frequency framework for climate resilient infrastructure
Bibliographic record
Abstract
Traditional design flood estimation approaches assume stationary conditions and rely on historical climate data, potentially under- or over-estimating flood risks. This study proposes a regression-based Regional Flood Frequency Analysis (RFFA) framework to assess projected changes in spring flood quantiles under various warming scenarios. The methodology integrates five distinct regression models using Bayesian Model Averaging (BMA) to establish functional relationships between flood quantiles and catchment physio-climatic variables. Focusing on Ontario’s prevalent spring floods, the BMA-RFFA framework demonstrates satisfactory predictive performance across flood quantiles. Future flood quantiles are projected using climatic data from CanRCM4 large ensemble simulations, capturing ensemble spread under a single model and scenario. Results indicate a nearly even distribution of median flood quantile changes from −13 % to +20 % under different warming scenarios, with increases in southern and western regions and decreases in the central region, highlighting spatial variability. Climatic variables such as seasonal water storage and rainfall intensity are identified as key predictors of future flood behavior. Continuous design flood changes over 10-year intervals from 1950 to 2100 were derived using an ensemble pooling approach, capturing the temporal evolution of flood quantiles driven by climate ensemble variability. The study also examined the contributions of Anthropogenic Climate Change (ACC) and Internal Climate Variability (ICV), revealing ICV as dominant in most catchments, while ACC shows stronger influence in western regions. Overall, the proposed framework provides enhanced estimation of future flood quantiles and a robust assessment of their changing behavior under projected climate variability, supporting improved flood risk assessment and infrastructure resilience.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 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".