Quantile Regression in Regional Frequency Analysis: A Better Exploitation of the Available Information
Bibliographic record
Abstract
Abstract Classical regression models are widely used in hydrological regional frequency analysis (RFA) in order to provide quantile estimates at ungauged sites given physio-meteorological information. Since classical regression-based methods only provide the conditional mean of the response variable, estimated at-site quantiles at gauged sites are commonly used to calibrate the regression models in RFA. Generally, only at-site quantiles estimated with long data records are retained for the calibration and the evaluation steps, whereas hydrological information from stations with few data is ignored. In addition, even if the at-site quantiles are estimated with long data series, they are always subject to model selection and parameter estimation. Hence, their use for the calibration of the RFA models may induce significant uncertainties in the modeled relationships. The aim of this paper is to propose a quantile regression (QR) model that gives directly the conditional quantile for RFA and avoids using at-site estimated quantiles in the calibration step. The proposed model presents another advantage where all the available hydrological information can be used in the calibration step including stations with very short data records. An evaluation criterion using observed data is also proposed in a cross-validation procedure. The proposed QR model is applied on a dataset representing 151 hydrometric stations from the province of Quebec and compared with a classical regression model. According to the proposed evaluation criterion, the QR is shown to be a viable model for regional estimations. Indeed, the proposed model proved to be robust and flexible, allowing for consideration of all the region’s sites, even those with extremely short flood records.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".