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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".