Resilience Quantification of Low-Impact Development Systems Using SWMM and a Probabilistic Approach
Bibliographic record
Abstract
Over the last few decades, there has been an increased demand for resilient low-impact development (LID) systems for stormwater management. During extreme uncertain events, a resilient LID system is expected not only to handle immediate stressors but also to rapidly adapt through changing and regulating itself to ensure continuous functionality. This study presents a new resilience quantification approach applicable to different LID systems. To demonstrate its utility, the developed approach was applied on a bioretention system. A set of equations for the LID system’s functionality was developed, integrating an analytical probabilistic approach (APA) and the stormwater management model (SWMM) continuous simulation output. These equations were subsequently used to evaluate resilience indices such as robustness, rapidity, serviceability, and the LID system’s reliability for different LID area ratios and surface depression storage depths. Both APA and SWMM exhibited similar resilience index values of 0.66–1.0 and 0.73–1.0, respectively. The overall reliability index values ranged from 60.50% to 100% when using SWMM and 56.67% to 100% when using APA, reflecting their consistency in predicting excellent system performance throughout the simulation period. However, the average rapidity index value prediction with APA was lower compared to SWMM. This slight variation was due to event-by-event hydrological simulation in APA, unlike the time step-by-time step continuous simulations in SWMM. The developed approach and findings of this study provide policy-makers with a consistent methodology to design resilient LID systems and empower decision-makers to strategize investment focused on optimized LID resilience-based designs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".