Fuzzy analytic hierarchy process framework for quantifying the flood resilience of housing infrastructure systems
Bibliographic record
Abstract
Housing constitutes a basic need for all living beings. Unfortunately, natural hazards, including floods, pose a severe threat to housing infrastructure systems. In turn, this paper develops a framework to quantify the resilience of housing infrastructure systems against flood hazards. The parameters for this resilience are based on the literature and knowledge from experts. This paper gauges the significance of each resilience parameter by using the analytic hierarchy process (AHP) and the fuzzy AHP. The evaluated values are then compared to observe the effectiveness of fuzzy AHP over AHP. The evaluated importance of each parameter will help stakeholders focus on the most important parameters and, in turn, boost the flood resilience of infrastructure. This paper then implements the developed framework in a study area to quantify local flood resilience. This resilience value will help stakeholders in the considered area to understand the resilience of local housing infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".