Characterization of 1-h rainfall temporal patterns using a Kohonen neural network: a Québec City case study
Bibliographic record
Abstract
After only a few years of operation, an extensive rain gauge network provides fruitful information on temporal patterns of local storms, helping urban water, managers with in the difficult choice of appropriate design storms. A total of 1470 1-h storms were identified for the period 1999–2005 in Québec City based on rainfall depth and interevent time criteria. Taking advantage of a clustering technique, the Kohonen neural network, 1-h storms were divided into 16 clusters depending on similarities in their temporal patterns, and then lumped into four groups. The database revealed that about one-third of all storms have a uniform intensity, one-third are early-peaking, and one-third are either symmetrical or late-peaking. Early-peaking patterns include the highest maximal 5-min intensity: 0.22–0.30 of the rainfall depth range, therefore in the same range as common Canadian 10-year design storms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".