swmm_mpc is a python package that can be used to perform model predictive control (MPC) for EPASWMM5 (Environmental Protection Agency Stormwater Management Model). swmm_mpc relies on the pyswmm package which enables the step by step running of SWMM5 models through Python.

swmm_mpc has only been tested Python 2.7 on Ubuntu 16.10 and RedHat. The modified version of OWASWMM5 included in the pyswmm library was compiled on Ubuntu 16.10 and will therefore will not work in a Windows environment.

git clone https://github.com/UVAdMIST/swmm_mpc.git
pip install swmm_mpc/

swmm_mpc requires a special version of OWA's pyswmm. To install this do:

pip install git+https://github.com/UVAdMIST/pyswmm.git@feature_save_hotstart

You will also need to have a working version of EPASWMM5 on your machine and have it added to the path. You can download the source code from the EPA Website. In Linux you can do this as follows:

wget https://www.epa.gov/sites/production/files/2017-03/swmm51012_engine_2.zip
unzip swmm51012_engine_2.zip -d swmm5
cd swmm5/
unzip source5_1_012.zip -d src
unzip makefiles.zip -d mk
cd mk
unzip GNU-CLE.zip -d gnu
cp gnu/Makefile ../src/
cd ../src

Then follow the instructions editing the swmm5.c line to read:

#define CLE
\\#define SOL
\\#define DLL

Then do

make

To add to the path, add this line to your .bashrc

export PATH="/path/to/swmm5/src:$PATH"

The run_swmm_mpc function is the main function (maybe the only function) that you will likely use. Here is an example of how I use it. First I write a small script to indicate the model and the different parameters I want to use, like this (called my_swmm_mpc.py in the next step):

from swmm_mpc.swmm_mpc import run_swmm_mpc

inp_file = "/path/to/my/model.inp"
control_horizon = 1. #hr
control_time_step = 900. #sec
control_str_ids = ["ORIFICE R1", "ORIFICE R2"]
results_dir = "/path/to/results/"
work_dir = "/path/to/work/"
ngen = 4
nindividuals = 300

target_depth_dict={'Node St1':{'target':1, 'weight':0.1}, 'Node St2':{'target':1.5, 'weight':0.1}}



def main():
    run_swmm_mpc(inp_file,
                 control_horizon,
                 control_time_step,
                 control_str_ids,
		 work_dir,
                 results_dir,
                 target_depth_dict=target_depth_dict,
                 ngen=ngen,
                 nindividuals=nindividuals
                 )

if __name__ == "__main__":
    main()

Then to run it, you simply call the script with python:

python my_swmm_mpc.py

A Docker image with swmm_mpc and all of its dependencies can be found at https://hub.docker.com/r/jsadler2/swmm_mpc/. You would run it like so:

docker run -v /path/to/inputfile.inp:/path/to/inputfile.inp -v /path/to/workdir/:/path/to/workdir/ -v /path/to/results/dir/:/path/to/results/dir/ -v /path/to/run_script.py:/run_script.py jsadler2/swmm_mpc:latest python /run_script.py

An example use case model is found on HydroShare: https://www.hydroshare.org/resource/73b38d6417ac4352b9dae38a78a47d81/.