Floodplain Width Prediction in Flood Disaster Management
Bibliographic record
Abstract
The accurate prediction of floodplain width is vital for sustainable urban development and mitigating flood risks, both crucial for achieving net-zero emissions targets. Urbanization exacerbates flood risks by altering hydrological systems, while climate change intensifies these challenges with unpredictable rainfall patterns and increased river flows. This study explores an innovative approach by integrating the Flexible Group Method of Data Handling (FGMDH) algorithm with 2D numerical hydrodynamic modeling (HEC-RAS) to improve floodplain width prediction. The study area, the Ottawa River, showcases the complex interplay of urban development, climate variability, and river morphology in flood risk dynamics. The FGMDH model, trained on the HEC-RAS dataset, demonstrated superior predictive capabilities, outperforming traditional Group Method of Data Handling (GMDH) models. Performance metrics indicated an R ² of 0.99 and a normalized root mean square error (NRMSE) of 4.68% for training and 4.61% for testing datasets, achieving “Very Good” descriptive performance levels. Compared to GMDH, FGMDH reduced prediction errors significantly, with mean absolute percentage error (MAPE) values of 4.80% (training) and 4.72% (testing). This integrated approach highlights the potential of AI-driven models to address complex flood dynamics under evolving climatic and urbanization patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".