Prediction of Extreme Events in Hydrologic Processes that Exhibit Abrupt Shifting Patterns
Bibliographic record
Abstract
We propose a probabilistic framework for modeling extreme events such as annual maximum floods, and annual low flows. The model assumes that the underlying data sequence exhibits abrupt changes or shifts in the mean, and the data are skewed and autocorrelated. Thus, the stochastic model is assumed to shift abruptly from one “stationary” state to another one around a long-term mean. The proposed modeling framework is based upon the previously suggested shifting mean (SM) models, where the process was assumed to be autocorrelated but the marginal distribution was normally distributed and as a result the model skewness was zero. The main objective of the research reported herein has been to further extend the referred SM models to incorporate skewed marginal distributions so that they can be applicable for frequency analysis of extreme events. For this purpose, two SM models and alternative estimation procedures were developed using the generalized extreme value, Pearson III, and Gumbel distributions. The proposed models utilizing skewed distributions are successfully applied for determining extreme quantiles of the quarter-monthly maximum annual outflows of Lake Ontario and the 7day annual low flows for the Paraná River in Argentina.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".