Regional Intensity-Duration-Frequency Curves Derived from Ensemble Empirical Mode Decomposition and Scaling Property
Bibliographic record
Abstract
This paper proposes deriving regional intensity-duration-frequency (IDF) curves for Edmonton, Canada, based on the scaling property of precipitation data using ensemble empirical mode decomposition (EEMD). Selected sets of annual maximum precipitation data were decomposed by the EEMD to intrinsic mode functions (IMFs), and the scaling property was investigated. Next, representative scale exponents were extracted. The results show that quantiles derived from general extreme value (GEV) probability distribution (PD) with parameters derived by the probability-weighted moment (PWM) are more accurate than those derived from the extreme value type I (EVI) PD with parameters derived by the method of moment (MOM), whose underestimation of rainfall intensity becomes obvious for high return period (greater than 25 years) and short duration (less than 1 h). The results also show that for Edmonton, generally three of four IMFs of the precipitation data showed a simple scaling property, and regional IDF curves derived from the scaling IDF and EEMD approach predict accurate storm intensities for rain-gauging sites at both the calibration and validation stages, but there could be errors associated with predicted storms of high return periods (100 year).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".