Simulation of extreme flood events for risk assessment for flood control of a reservoir-lake-river system under spatially dependent uncertainties
Bibliographic record
Abstract
Study region Chaohu Basin, the lower reach of Yangtze River region, China. Study focus This study proposes an integrated framework to assess extreme flood risks in a reservoir-lake-river system under spatially dependent uncertainties and data scarcity. A Vine copula is applied to model the spatial dependencies across multiple risk sources in extreme flood events. Scenario trees are constructed using the Neural Gas Algorithm to generate the representative extreme flood scenarios. These scenarios are incorporated into a flood control optimization model to assess the flood risks under extreme flood scenarios. New hydrological insights for the region The results show that the proposed method can effectively simulate the spatial interdependencies of risk source variables based on limited historical data. In addition, the scenario trees effectively extract representative flood scenarios. The flood risk probabilities for the reservoir and lake under extreme compound flood events (with occurrence probabilities below 0.02) are assessed to be 0.0072 and 0.0182, respectively. Compared to random sampling and K means clustering, the proposed method achieves the lowest continuous ranked probability scores (CRPS), indicating higher reliability. This study provides an efficient and systematic approach for flood risk assessment in the Chaohu Basin and can support flood management in similar reservoir-lake-river systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".