swmm-uncertainty

# SWMM Uncertainty Part of [Agentic SWMM](https://github.com/Zhonghao1995/agentic-swmm-workflow) — install the project first for the executable toolchain (aiswmm CLI, SWMM solver, MCP servers). ## What this skill provides - User-defined fuzzy membership functions for SWMM parameters. - Baseline-aware triangular fuzzy numbers, where the current model value is the default triangle peak. - Alpha-cut transformation from fuzzy membership functions to parameter intervals. - LHS, random, or boundary sampling inside each alpha-cut interval. - Monte Carlo parameter sampling for prior or calibration-informed probability distributions. - Normal/lognormal/truncated-normal/uniform sampling with simple physical constraints such as bound parameters and greater-than rules. - Batch propagation through SWMM by reusing the existing calibration patch-map convention. - Normalized Shannon entropy metrics for output ensembles, such as hydrograph entropy over time. - Machine-readable uncertainty summaries for output envelopes, entropy records, and failed/invalid samples. - Sensitivity-analysis screening with three sub-methods (OAT / Morris / Sobol') sharing one entry point (`scripts/sensitivity.py`). - Rainfall-forcing ensembles: time-series perturbation of an observed rainfall record (gaussian, multiplicative, AR(1), intensity_scaling) or IDF-curve sampling of design storms (Chicago / Huff / SCS Type II), with optional per-realisation SWMM runs and ensemble envelope aggregation. This skill is i...