On Risk Analysis of Water Resources Systems Under Non-Stationary Conditions
Bibliographic record
Abstract
Traditionally, uncertainty-related analyses of water resources systems, such as flood frequency analyses for mitigation, are performed under stationary conditions, where, statistical properties, such as the means and variances of random variables involved are assumed to be constant with time. In some cases, due to natural and artificial influences, hydrometric data are reportedly experiencing shifts, trends or other changes, even on an annual time scale. In identifying sustainable management solutions for water resources systems it is important to recognize impacts of such changes on risks of system failure. This information may be particularly valuable for long-term planning of water resources projects. Methods of assessing risks of water resources systems are summarized herein. The work identifies risk analyses for systems with different characteristics, static or dynamic, and non-repairable or repairable. It is shown that a stochastic point process is an effective tool for risk analyses of systems characterized by non-stationary conditions. Risk analyses of repairable systems with long-term non-stationarities, representative of many cases in the water resources engineering, have not been extensively investigated. A marked inhomogeneous alternating renewal process is shown to be suitable for such cases, and the discussion of this process presented herein provides a foundation for further exploration of its applicability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".