Background
Urbanization expands impervious surfaces and intensifies urban flood risk. Permeable pavement is a proven LID measure — but where and how much to install, under a fixed budget, is a separate decision problem.
Approach
- Automated geospatial processing (OSM + Copernicus DEM) builds EPA SWMM for 18 Seocho-gu sub-catchments
- Scenario sweep across 0–100% coverage under a 10-year design storm
- XGBoost surrogate (R² = 0.79) trained to replace expensive SWMM runs
- NSGA-II multi-objective optimization yields the Pareto front (runoff reduction vs installation area)
- SHAP quantifies per-sub-catchment influence
Key Results
| Metric | Value | |---|---| | Baseline runoff | 614,298 m³ | | Runoff reduction at 100% coverage | 24.8% | | Share delivered by first 30% coverage | 73% | | Cost-effective sweet spot | 30–50% | | Pareto runoff reduction range | 14.7–22.7% | | Pareto installation area range | 3.8–63.5% | | SHAP highest-impact sub-district | Naegok-dong (large catchment) |
Implications
- Permeable pavement exhibits strong diminishing returns — the planning message is "30–50% is optimal," not "the more, the better."
- Influence is non-uniform across sub-catchments; SHAP identifies priority investment locations.
- The workflow is reproducible, scalable, and interpretable — a deployable methodology for LID spatial optimization.
Related solutions
- Spatial Optimization of Green Infrastructure — core workflow used here
- Stormwater Risk Analysis (SWMM · Neural ODE) — underlying simulation foundation