FUZZY RELIABILITY ANALYSIS FOR THE EVALUATION OF WATER RESOURCE SYSTEMS PERFORMANCE
Bibliographic record
Abstract
One of the main goals of water resources systems design is to ensure that a system performs satisfactorily under a wide range of possible future conditions. Water resource systems exhibit a high level of spatial complexity. Many elements of complex water resource systems are vulnerable to temporary disruption in service due to natural hazards or human error whether unintentional as in the case of operational errors and mistakes or due to intentional causes such as a terrorist act. Engineering risk and reliability analysis is a general methodology for the quantification of uncertainty and the evaluation of its consequences for the safety of engineering systems. The first step in any risk analysis is to identify the risk, clearly detailing all sources of uncertainty that may contribute to the risk of failure. The quantification of risk is the second step where the effects of the uncertainties are measured using different system performance indices and figures of merit. This presentation explores the utility of fuzzy set theory in the field of water resource reliability analysis and proposes three fuzzy reliability indices: (1) a combined reliability-vulnerability index, (2) a robustness index, and (3) a resiliency index. These indices were successfully tested using a case study of London regional water supply system. Extension of these three indices to spatial evaluation of uncertainty involved in flood plain management is presented using a case study of the Medway creek (London, Ontario).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".