Assessment of low-impact development practices on stormwater management using SWMM 5.2: a case study of Shiraz, Iran
Bibliographic record
Abstract
As the world's population continues to grow and urban areas expand, climate change has become an increasingly urgent challenge. The change in land use due to urbanization can increase surface runoff volume, resulting in urban flooding. Additionally, climate change increases the likelihood of extreme rainfall and inadequate stormwater drainage systems can worsen flood risk. Therefore, the adoption of innovative stormwater management techniques is imperative. Recently, there has been a move towards some approaches such as Low Impact Development (LID) that aims to reduce the strain on stormwater infrastructure. This study aimed to model stormwater management using EPA SWMM 5.2, focusing on a case study of Shiraz, Iran originating from a semi-arid zone watershed. To mitigate the escalating peak stormwater and address the limitations of existing drainage systems, different LID methods have been employed in the study area under 4 different return periods (5, 25, 50, and 100 years). The results underscore that LID methods have resulted in a decrease in flooding volume within the study area across different return periods, ranging from 13.83% to 54.65%. The findings show that the integration of permeable pavements and bioretention cells was highly effective in managing water flow and it approximately decreased watershed total runoff volume by 42% corresponding to all return periods. This research offers insights into tailored stormwater management for rapidly urbanizing areas. Shiraz's successful LID techniques can serve as a model for cities facing similar challenges, enhancing their resilience to urban floods, and promoting sustainable water management amid urban growth and climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".