Introduction of a stochastic approach in the development of a numerical model for tidal–fluvial interaction analysis and design
Bibliographic record
Abstract
The interaction of tidal surges and fluvial flows in any river system results in a prevailing combined condition that requires accurate consideration in the reaches of the system not directly influenced by either phenomenon. The combined return period of the system should be deduced based on the combined effects of the two phenomena, which may usually be considered independent of one another. It is therefore imperative to consider both the return periods of the upstream flood condition and the downstream tidal surge condition to determine the combined return period for design flood analysis in tidal river systems. The task of obtaining a suitable and practical combination of the two phenomena encompasses preparation of an interactive scenario most closely and practically verified against the actual design event. In the present research, a combination coefficient has been introduced using the joint probability method. The combination coefficient is used to combine return periods of tidal surges and fluvial floods. A numerical and stochastic model has been developed for hydraulic routing in tidal rivers. The model is applied to the Karun River in Iran and the Severn River in the United Kingdom. The model can generally be utilized for river management and determination of safe bank height for tidal rivers.Key words: combination coefficient, tidal surges, river floods, joint probability method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".