A Multi-Dimensional Contribution-Based Framework for Evaluating Urban Stormwater Management Efficiency
Bibliographic record
Abstract
Urbanization and climate change amplify urban flooding risks, demanding efficient, data-minimal tools to strengthen flood resilience. This study presents a pioneering multi-dimensional framework that quantifies the contributions of source reduction, stormwater pipes, and drainage/flood control systems, circumventing the need for intricate hydrological models. Leveraging rainfall depth (mm), runoff volume (m3), and peak flow rate (m3/h) provides a comprehensive evaluation of stormwater management efficacy. Applied to a hypothetical city, City A, under 30- and 50-year rainfall scenarios, the framework reveals efficiencies of 91.0% for rainfall depth and runoff volume, and 90.8% for peak flow in the 30-year case (9% shortfall), declining to 75.7% peak flow efficiency with a 24.3% deficit in the 50-year scenario, underscoring constraints in extreme-event response. Contributions analysis shows stormwater pipes (42.8–47.6%, mean: 46.0%) and drainage/flood control (40.8–43.2%, mean: 41.6%) predominate, while source reduction adds 11.6–14.0% (mean: 12.4%). A primary contribution lies in reducing data demands by approximately 70% compared to traditional approaches, rendering this framework a practical, scalable solution for flood management and sponge city design in data-limited settings. These findings elucidate system vulnerabilities and offer actionable strategies, advancing urban flood resilience both theoretically and practically.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".