SWMM Model Simplification

Algorithm to simplify high resolution SWMM models using the python package swmm-api.

This is the code of the algorithm publish within this scientific article:

Pichler, M., König, A. W., Reinstaller, S., & Muschalla, D. (2024). Fully automated simplification of urban drainage models on a city scale. Water Science & Technology.

This repository is structures as followed:

• Main Script: swmm_model_simplification.aggregate_model.aggregate_model()
• Folder helpers are functions which are needed in the main script.
• Folder evaluation are functions and plot to evaluate the accuracy of the simplification algorithm.
• Folder workflows are functions for the main sub-processes in the workflow.
• Example for model Bellinge¹ in example / simplify_bellinge.ipynb or simplify_bellinge.py

  Note that model "Bellinge" not well suited for this application, but is used due to the availability of the model.

Why is the code not an installable package?

Because every model is different and has its own peculiarities and therefore the script has to be adapted, which does not work with an installed package. So this code should be used as template.

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The main sequence of the functions called is:

simplifying_model > consolidate_conduits > aggregate_subcatchments > calibrate_conduits > calibrate_subcatchments

For documentation please read the comments in the code and the function headers.

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Possible extension:

• allow multiple metrics for width calibration (as function in config)
  • must replace function calibrate_subcatchments()

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Python Requirements

Packages required for this program will be installed with pip during the installation process and can be seen in the requirements.txt file.

flowchart TD
    classDef missing stroke-dasharray: 5
    joblib["joblib<br>1.3.2"]
    networkx["networkx<br>3.1"]
    numpy["numpy<br>1.26.1"]
    packaging["packaging<br>24.1"]
    pandas["pandas<br>2.2.3"]
    python-dateutil["python-dateutil<br>2.8.2"]
    pytz["pytz<br>2023.3.post1"]
    scikit-learn["scikit-learn<br>1.3.1"]
    scipy["scipy<br>1.11.3"]
    shapely["shapely<br>2.0.2"]
    six["six<br>1.16.0"]
    swmm-api["swmm-api<br>0.4.58"]
    threadpoolctl["threadpoolctl<br>3.2.0"]
    tqdm["tqdm<br>4.67.0"]
    tzdata["tzdata<br>2023.3"]
    pandas -- "&ge;1.26.0" --> numpy
    pandas -- "&ge;2.8.2" --> python-dateutil
    pandas -- "&ge;2020.1" --> pytz
    pandas -- "&ge;2022.7" --> tzdata
    python-dateutil -- "&ge;1.5" --> six
    scikit-learn -- "&ge;1.1.1" --> joblib
    scikit-learn -- "&ge;1.17.3,&lt;2.0" --> numpy
    scikit-learn -- "&ge;1.5.0" --> scipy
    scikit-learn -- "&ge;2.0.0" --> threadpoolctl
    scipy -- "&ge;1.21.6,&lt;1.28.0" --> numpy
    shapely -- "&ge;1.14" --> numpy
    swmm-api -- "any" --> packaging
    swmm-api -- "any" --> pandas
    swmm-api -- "any" --> tqdm

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Footnotes

1. Nedergaard Pedersen, A., Wied Pedersen, J., Vigueras-Rodriguez, A., Brink-Kjær, A., Borup, M., & Steen Mikkelsen, P. (2021). The Bellinge data set: Open data and models for community-wide urban drainage systems research. Earth System Science Data, 13(10), 4779–4798. https://doi.org/10.5194/essd-13-4779-2021 ↩