swmm-calibrationlisted
Install: claude install-skill Zhonghao1995/agentic-swmm-workflow
# SWMM Calibration / Validation (MVP scaffold)
## What this skill provides
- A practical calibration scaffold around the existing SWMM runner workflow.
- A strict calibration boundary: calibration and validation require observed data. Without observed flow, depth, soil-moisture, or volume data, use `swmm-uncertainty` for prior uncertainty propagation instead of calling the run calibrated.
- Observed-flow ingestion from delimited text files (`.csv`, `.tsv`, `.dat`, whitespace-separated text).
- Metric calculation for simulated vs observed hydrographs:
- **KGE** (Kling-Gupta Efficiency) + (r, alpha, beta) decomposition — primary metric for publication-grade calibration.
- NSE
- RMSE
- Bias / PBIAS%
- Peak flow error
- Peak timing error
- Simple INP text patching using an explicit mapping from parameter names to line selectors.
- Batch evaluation of candidate parameter sets for:
- `sensitivity`
- `calibrate`
- `validate`
- Bounded internal search for calibration candidate generation:
- `search --strategy random` — uniform random sampling (fast prototyping).
- `search --strategy lhs` — Latin Hypercube Sampling (fast prototyping).
- `search --strategy adaptive` — multi-round LHS refinement around elite trials (fast prototyping).
- `search --strategy sceua` — Shuffled Complex Evolution (SCE-UA); recommended for publication-grade point-estimate calibration. Minimises `(1 - KGE)` via `spotpy.algorithms.sceua` and emits a `calibration_summary.json` with KGE