Evaluating Urban Flood Resilience within the Social-Economic-Natural Complex Ecosystem: A Case Study of Cities in the Yangtze River Delta
Bibliographic record
Abstract
With global climate change and rapid urbanization, it is critical to assess urban flood resilience (UFR) within the social-economic-natural complex ecosystem in dealing with urban flood disasters. This research proposes a conceptual framework based on the PSR-SENCE model for evaluating and exploring trends in urban flood resilience over time, using 27 cities in the Yangtze River Delta (YRD) of China as case studies. For the overall evaluation, a hybrid weighting method, VIKOR, and sensitivity analysis were used. During that time, UFR in the YRD region averaged a moderate level with an upward trend. This distinguishes between the resilience levels and fluctuation trends of provinces and cities. Jiangsu, Zhejiang, and Anhui provinces all displayed a trend of progressive development; however, Shanghai displayed a completely opposite pattern, mainly because of resilience in the state dimension. During that time, 81.41% of cities exhibited varying, upward trends in urban flood resistance, with few demonstrating inverse changes. Regional, provincial, and city-level implications are proposed for future UFR enhancement. The research contributes to a better understanding of the urban complex ecosystem under flood conditions and provides significant insights for policymakers, urban planners, and practitioners in the YRD region and other similar flood-prone urban areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".