Stormwater Symposium

Oral

396360 - A python-based autocalibration routine supporting the stormwater management model

Monday, June 4
4:00 PM - 5:30 PM
Location: Mirage Room
Co-Authors: Ben Hodges, Austin, Texas – University of Texas at Austin

The stormwater management model (SWMM) is a clustered model that relies on subcatchment-averaged parameter assignments to correctly capture catchment stormwater runoff behavior. Model calibration is considered a critical albeit arduous step for SWMM performance, one that that stormwater management designers often undertake manually. This research presents an open-source, auto-calibration routine that increases the efficiency and accuracy of the model calibration process. The routine first represents the subcatchment network as graph objects using the NetworkX python package for flexibility in handling real-world calibration data availability. Once the calibrate-able subset of the system is identified, a multi-objective function, genetic algorithm (modified Non-Dominated Sorting Genetic Algorithm II) determines the Pareto front for the objective functions within the parameter space. The solutions on this Pareto front represent the optimized parameter value sets for the catchment behavior that could not have been reasonably obtained through manual calibration. A specific solution among this Pareto set can be chosen by assigning weights to the objective functions.

Edward D. Tiernan

Research Assistant
University of Texas at Austin

My name is Edward Tiernan, I am a Master's student at the University of Texas at Austin working with Dr. Ben R. Hodges in collaboration with the National Center for Infrastructure Management and Modeling (NCIMM). My undergraduate studies were in Civil and Environmental Engineering from the University of Virginia in Charlottesville, VA. This background cultivated in me an interest in hydrology and system modeling, an interest that Dr Hodges work with NCIMM revamping and upgrading the Stormwater Management Model (SWMM) allowed me to persue. The primary goal of NCIMM is to develop the next generation of SWMM and EPANET, two leading hydrologic and infrastructure managment models, with special emphasis on automating the model workflows. My research at UT Austin developing SWMMCALPY, an automated SWMM calibration tool written in python, is directly in line with the overall NCIMM objectives.

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396360 - A python-based autocalibration routine supporting the stormwater management model



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