Uncertainty-based flood resiliency evaluation of wastewater treatment plants
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) have a significant role in urban systems’ serviceability. These infrastructures, especially in coastal regions, are vulnerable to flooding. To minimize vulnerability, a better understanding of flood risk must be realized. To quantify the extent of efforts for flood risk management, a unified index is needed for evaluating resiliency as a key concept in understanding vulnerability. Here, a framework is developed to evaluate the resiliency of WWTPs in coastal areas of New York City. An analysis of the current understanding of vulnerability is performed and a new perspective utilizing different components including resourcefulness, robustness, rapidity, and redundancy is presented to quantify resiliency using a multi-criteria decision-making (MCDM) technique. To investigate the effect of certain factors of WWTPs on resiliency, uncertainty analysis is also incorporated in developing the framework. As a result, rather than a single value, a range of variation for each WWTP's resiliency is obtained. Finally, improvement of WWTPs’ performance is investigated by allocating financial resources. The results show the significant value of quantifying and improving resiliency that could be used in development of investment strategies. Consideration of uncertainty in the analysis is of great worth to estimate the potential room for improvement of resiliency of individual WWTPs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".