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/fec1e3de-9586-4a61-b3a1-02382592e52c

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

Dataset

• Civil Engineering
• Engineering

Graph Neural Network, Surrogate model, SWMM, Urban drainage simulation

CC BY 4.0

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

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

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.

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

4TU.ResearchData

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

Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks

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