The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/989a0d3d-3b4d-47c7-8677-31c5975f9dec

Garzón, Alexander; Kapelan, Zoran ; Langeveld, Jeroen; Taormina, Riccardo (2024): SWMM GNN metamodel – Code for paper: Transferable and Data Efficient Metamodeling of Storm Water System Nodal Depths Using Auto-Regressive Graph Neural Networks. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/989a0d3d-3b4d-47c7-8677-31c5975f9dec.v1

Software

• Civil Engineering
• Artificial Intelligence and Image Processing
• Computer Software
• Engineering
• Information and Computing Sciences

GNN, Graph neural networks, Machine learning, Metamodel, Surrogate model, SWMM, Time series, Urban drainage systems

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Alexander Garzón , Zoran Kapelan , Jeroen Langeveld , Riccardo Taormina

Repository of the code for the GNN based metamodel of SWMM. This code is linked to the paper "Transferable and Data Efficient Metamodeling of Storm Water System Nodal Depths Using Auto-Regressive Graph Neural Networks" by Alexander Garzón, Zoran Kapelan, Jeroen Langeveld, and Riccardo Taormina.

This repository contains the code for developing machine learning metamodels of SWMM.

In brief, this code allows to create a dataset from SWMM simulations, train a machine learning model, and evaluate the model. The code is designed to work with SWMM simulations of storm water systems. The code is based on PyTorch and PyTorch Geometric.

This work is supported by the TU Delft AI Labs programme.

This repository was supported by the Digital Competence Centre, Delft University of Technology.

• 2024-09-12 first online, published, posted

4TU.ResearchData

Transferable and Data Efficient Metamodeling of Storm Water System Nodal Depths Using Auto-Regressive Graph Neural Networks

• https://github.com/alextremo0205/SWMM_GNN_Repository_Paper_version

https://doi.org/10.4121/fec1e3de-9586-4a61-b3a1-02382592e52c

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management