Assessment of Critical Infrastructure Resilience to Flooding Using a Response Curve Approach
Bibliographic record
Abstract
Following a flood the functioning of critical infrastructure (CI), such as power and transportation networks, plays an important role in recovery and the resilience of the city. Previous research investigated resilience indicators, however, there is no method in the literature to quantify the resilience of CI to flooding specifically or to quantify the effect of measures. This new method to quantify CI resilience to flooding proposes an expected annual disruption (EADIS) metric and curve of disruption versus likelihood. The units used for the EADIS metric for disruption are in terms of people affected over time (person × days). Using flood modelling outputs, spatial infrastructure, and population data as inputs, this metric is used to benchmark CI resilience to flooding and test the improvement with resilience enhancing measures. These measures are focused on the resilience aspects robustness, redundancy and flexibility. Relative improvements in resilience were quantified for a case study area in Toronto, Canada and it was found that redundancy, flexibility, and robustness measures resulted in 44, 30, and 48% reductions in EADIS respectively. While there are limitations, results suggest that this method can effectively quantify CI resilience to flooding and quantify relative improvements with resilience enhancing measures for cities.
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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 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".