Depth-based regional index-flood model
Bibliographic record
Abstract
[1] Regional flood frequency analysis aims to estimate flood risk at sites where little or no hydrological data are available. The index-flood model is one of the commonly employed models for this purpose. In this model, the predicted value depends on the growth curve and its regional parameters. The latter are estimated as weighted averages of the at-site parameters. Traditional approaches are mainly based on site record lengths or region size to define these weights. Hence, they are not representative of the hydrological similarity between sites within a region. In addition, they are not defined to reach optimality in terms of model performance. To overcome these limitations, the present paper aims to propose a new optimal iterative weighting scheme to the index-flood model. The proposed approach is based on a number of elements: a statistical depth function to introduce similarity between sites, a weight function to amplify and control the depth values, an iterative procedure to improve estimation accuracy, and an optimization algorithm to objectively automate the choice of the weight function. A data set from the Island of Sicily (Italy) is used to compare the proposed approach with traditional ones. On the basis of the L-moments and using cluster analysis techniques, the studied region is subdivided into three homogeneous subregions. The results indicate that the proposed approach performs significantly better than traditional ones both in terms of relative bias and relative root mean squares error. The proposed approach allows identification of cross correlation in the region and provides a significant performance improvement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".