Advancing Flood Warning in Rural Watersheds: A Data-Driven Machine Learning Classification Framework
Bibliographic record
Abstract
Riverine flooding jeopardizes the health and livelihood of communities and environmental systems worldwide and is expected to become more frequent and intense as climate change conditions alter hydrological processes. Most regions worldwide have flood warning systems that incorporate streamflow thresholds to specify action for emergency management and public messaging. With prescribed thresholds, there is an opportunity to apply classification Machine Learning (ML) models for flood message forecasting to aid in flood warning systems. In this study, lagged surface and subsurface data were used to develop a framework for employing classification ML models to predict types of flood warning messages in a rural watershed in Ontario, Canada. Thirty-three classification ML algorithms were implemented on a five-year dataset to predict the flood warning message six hours in advance. Overall classification accuracy ranged from 89.5% to 99.7%, where the top performing models were fine K-Nearest Neighbour (KNN), ensemble subsurface KNN and cubic support vector machines. Feature sensitivity analysis of the top performing models showed that streamflow, hourly flow difference and groundwater elevation were important parameters for flood message prediction. Results from this study highlight that classification ML algorithms can be applied to small agricultural watersheds for predicting flood messages, and the importance of including subsurface parameters in flood forecasting models. The framework developed from this work can be applied to other regions to develop flood forecasting models with interpretable outputs for stakeholders including flood forecasting professionals, government officials, emergency managers and the public.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".