Estimating flood quantiles at ungauged sites using nonparametric regression methods with spatial components
Bibliographic record
Abstract
Prediction of flood quantiles at ungauged sites has been investigated using several nonparametric regression methods including: local regression based on regions of influence, neural networks and generalized additive models (GAM). These methods were used to describe the relationship between run-off variables and catchment descriptors to predict flood quantiles. Previous work reported the presence of spatial correlation in the residuals for these models. To this end, this study proposes and investigates ways of incorporating spatial components. An L-moments regression technique (LRT) is developed to predict L-moments of target sites and flood quantiles are derived by aggregating quantiles from multiple candidate distributions. The predictive power of the proposed methods is evaluated on a large database of Canadian rivers using cross-validation. The results are examined inside different provinces and hydrological regions to assess the behaviour of the methods. The results show that GAM and local regression using respectively thin plate spline and kriging provide the best predictive powers among the considered methods. Additionally, the LRT method is found to improve prediction power over the well-known index-flood model and has similar results to quantile regression techniques (QRT) when using the same nonparametric regression approaches.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".