Anticipatory detection and characterization of urban stormwater system flood risk: a model-based fault diagnosis approach

File Descriptions lab_system.inp - EPA SWMM model input file. phy_healthy_impulse_database.csv - Simulated fault-free stormwater system depth and velocity measurements using survey impulse. phy_impulse_2.5yr_fault_database.csv - Simulated stormwater system depth and velocity measurements using design storm impulse under fault conditions. phy_impuslse_fault_database.csv - Simulated stormwater system depth and velocity measurements using survey impulse under fault conditions. pipeline_diagnose.ipynb - Python Jupyter Notebook used to perform fault detection and diagnosis and produce results figures found in the paper. single_fault_diagnose.ipynb - Diagnose a single fault from a single flow measurement. Used to test the accuracy of compound fault diagnosis. create_fault_database.ipynb - Functions to simulate faults in the EPA SWMM model. example_system.inp - SWMM model used to simulate compound faults. compound_fault_<6,4,2,2>_<8,5,10,4>.csv - Simulated flow measurements for the compound fault scenarios. misspec_M<Manning's n value>_L<fault node>.csv - Simulated flow measurements for the misspecification cases. The Manning's n values is the maximum value used to in the uniform sampling to generate random roughness offsets. Pimpulse_M1_L30_R1.dat - Impulse input file for the SWMM model. M1 denotes the maximum flow rate of 1 cfs, L30 denotes a 30 min impulse length, and R1 is an obsolete index. fault_database.csv - Simulated stormwater system depth and velocity measurements using survey impulse under fault conditions used for misspecification studies. supplemental_materials.pdf - Supplemental materials for the manuscript.