Peak flow prediction using fuzzy linear regression: Case study of the Bow River
Bibliographic record
Abstract
The 2013 floods in Alberta highlighted the need for better flood prediction. Though the mechanisms behind floods and extreme events in urban areas are understood and documented, the uncertainty in data during these events makes it difficult to accurately predict and assess the risk of floods. In this research, a fuzzy number based linear regression model is proposed that incorporates uncertainty and characterizes risk of extreme events in the Bow River at Calgary, Alberta, Canada. The proposed model uses a fuzzy linear regression model to predict peak flow rate using mean daily flow rate. Lagged data from one to seven days is also considered. Results of the research show that using a fuzzy number approach to predict uncertain extreme events outperforms traditional regression methods in the Bow River at Calgary. The developed model can accurately predict daily peak flow, including a flood event in 2005, up to 7 days in advance. In addition to this, fuzzy number model output can be used to further characterize the risk of peak flow magnitude. These results are extremely beneficial for water resource managers who implement flood mitigation and defence strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".