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

DOI:10.4121/fec1e3de-9586-4a61-b3a1-02382592e52c.v1
The DOI displayed 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/fec1e3de-9586-4a61-b3a1-02382592e52c

Datacite citation style

Garzón, Alexander; Kapelan, Zoran ; Langeveld, Jeroen; Taormina, Riccardo (2024): Data for paper "Transferable and Data Efficient Metamodeling of Storm Water System Nodal Depths Using Auto-Regressive Graph Neural Networks". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fec1e3de-9586-4a61-b3a1-02382592e52c.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains data collected during the development of a Graph Neural Network metamodel of the software SWMM (Storm water management model) at the Delft University of Technology, as part of Alexander Garzón's PhD project, and with the corresponding publication "Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks" <https://doi.org/10.1016/j.watres.2024.122396>.


It is being made public both to act as supplementary data for publications and the PhD project of Alexander Garzón and in order for other researchers to use this data in their own work.


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


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

History

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

Publisher

4TU.ResearchData

Format

csv, txt, pickle files (.pk), PyTorch model weights (.pt), and Markdown (.md).

Organizations

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

DATA

Files (3)