The Resilience Performance Index, a Fuzzy Logic Approach to Assess Urban Resilience
Bibliographic record
Abstract
Urban resilience is recently a prominent issue due to rapid urbanization and increasing challenges and stressors affecting cities. Assessment of urban resilience is an essential step in enhancing resilience performance since regular assessment informs resilience action plans, determines areas of deficiencies, and provides spatial and temporal comparisons. However, resilience assessment is a complex process that requires intensive data and resources due to the multi-dimensional and dynamic nature of resilience, and the imprecision of resilience data. In this context, the research aims to develop The Resilience Performance Index (RPI), through setting a conceptual framework, defining relevant resilience indicators, and finally modelling resilience performance using The Fuzzy Logic Approach, aiming to combine resilience analysis with artificial intelligence (AI) tools and dynamic modelling methods. The RPI assesses both qualitative and quantitative resilience indicators obtained through records, census data or structured questionnaires. Indicators’ values are modelled through a designed fuzzy logic system to obtain the resilience performance score. The developed index is applied on New Damietta city to inform resilience action plans in the Nile Delta region. The RPI addresses the complexity of resilience assessment and ambiguity of resilience data through an easy applicable, user friendly approach without the need for complex mathematical and statistical methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".