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.

NOTE: You must have numpy installed already or this will not work

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

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 it is used. run_swmm_mpc takes one and only one argument: the path to your configuration file (see example below).

The configuration file is a json file that specifies all of the parameters you will be using in your swmm_mpc run. There are certain parameters that are required to be specified and others that have a default value and are not required.

1. inp_file_path: [string] path to .inp file relative to config file
2. ctl_horizon: [number] ctl horizon in hours
3. ctl_time_step: [number] control time step in seconds
4. ctl_str_ids: [list of strings] ids of control structures for which controls policies will be found. Each should start with one of the key words ORIFICE, PUMP, or WEIR e.g., [ORIFICE R1, ORIFICE R2]
5. work_dir: [string] directory relative to config file where the temporary files will be created. note: this must be an existing directory
6. results_dir: [string] directory relative to config file where the results will be written. note: this must be an existing directory
7. opt_method: [string] optimization method. Currently supported methods are 'genetic_algorithm', and 'bayesian_opt'
8. optimization_params: [dict] dictionary with key values that will be passed to the optimization function for GA this includes
   • ngen: [int] number of generations for GA
   • nindividuals: [int] number of individuals for initial generation in GA
9. run_suffix: [string] will be added to the results filename

1. flood_weight: [number] overall weight for the sum of all flooding relative to the overall weight for the sum of the absolute deviations from target depths (dev_weight). Default: 1
2. dev_weight: [number] overall weight for the sum of the absolute deviations from target depths. This weight is relative to the flood_weight Default: 0
3. target_depth_dict: [dict] dictionary where the keys are the nodeids and the values are a dictionary. The inner dictionary has two keys, 'target', and 'weight'. These values specify the target depth for the nodeid and the weight given to that in the cost function. e.g., {'St1': {'target': 1, 'weight': 0.1}} Default: None (flooding from all nodes is weighted equally)
4. node_flood_weight_dict: [dict] dictionary where the keys are the node ids and the values are the relative weights for weighting the amount of flooding for a given node. e.g., {'st1': 10, 'J3': 1}. Default: None

{
    "inp_file_path": "my_swmm_model.inp",
    "ctl_horizon": 1,
    "ctl_time_step": 900,
    "ctl_str_ids": ["ORIFICE R1", "ORIFICE R2"],
    "work_dir": "work/",
    "results_dir": "results/",
    "dev_weight":0.5,
    "flood_weight":1000,
    "run_suffix": "my_mpc_run",
    "opt_method": "genetic_algorithm",
    "optimization_params":
        {
                    "num_cores":20,
                    "ngen":8,
                    "nindividuals":120
                },
    "target_depth_dict":
        {
            "Node St1":
                {
                    "target":1.69,
                    "weight":1
                },
            "Node St2":
                {
                    "target":1.69,
                    "weight":1
                }
        }
} 

from swmm_mpc.swmm_mpc import run_swmm_mpc
run_swmm_mpc('my_config_file.json')

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 (this assumes your results_dir, your workdir, your .inp file, and your config file (*.json) are all in the same directory):

docker run -v /path/to/run_dir/:/run_dir/ -w /run_dir/ jsadler2/swmm_mpc:latest python /run_script.py

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