Trivariate probabilistic assessments of the compound flooding events using the 3-D Fully Nested Archimedean (FNA) copula in the semiparametric distribution setting
Bibliographic record
Abstract
Abstract Severe flooding in coastal areas can result from the joint probability of multiple extreme or non-extreme oceanographic, hydrological, and meteorological factors, resulting in compound flooding (CF) events if they occur successively or simultaneously. Recently, copula functions provided a much more flexible environment in joint modelling. Using copula-based bivariate probability distribution is ineffective in assessing the likelihood of joint occurrence, thus demanding a more advanced higher dimensional probability framework. Incorporating a higher dimensional copulas framework via traditional symmetric 3-D Archimedean or Elliptical copulas has statistical limits and would be incapable of preserving all lower-level dependencies. The heterogeneous dependency in CF events can be modelled effectively via the fully nested Archimedean (FNA) copulas. Incorporating FNA under parametric distribution settings is not flexible enough since it is restricted by the prior distributional assumption of the function type for both marginal density functions and copulas in parametric fittings. If their marginal density belongs to specific parametric family distribution, it could be problematic if underlying assumptions are violated. This study introduces a 3-D FNA copula simulation in the semiparametric setting by introducing nonparametric marginal distributions conjoined by a parametric copula density. The derived semiparametric FNA copula is applied in the trivariate modelling in compounding the joint impact of rainfall, storm surge and river discharge with 46 years of observations on the west coast of Canada. The performance of the derived model has also been compared analytically and graphically with the FNA copula constructed with parametric marginal density. It is concluded that the performance of FNA with nonparametric marginals outperforms the FNA copula built under parametric settings. The presented copula-based joint modelling is employed in multivariate analysis of flood risks in trivariate primary joint and conditional joint return periods. The trivariate hydrologic risk associated with compound events is analyzed using the failure probability (FP) statistics. Investigation reveals that trivariate hydrologic events produce a higher failure probability than bivariate (or univariate) events; neglecting trivariate joint analysis would underestimate FP. Also, it indicates that trivariate hydrologic risk values would increase with an increase in service time of the hydraulic facilities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".