TY - DATA
T1 - Data for paper "Transferable and Data Efficient Metamodeling of Storm Water System Nodal Depths Using Auto-Regressive Graph Neural Networks"
PY - 2024/09/12
AU - Alexander Garzón
AU - Zoran  Kapelan
AU - Jeroen Langeveld
AU - Riccardo Taormina
UR - 
DO - 10.4121/fec1e3de-9586-4a61-b3a1-02382592e52c.v1
KW - SWMM
KW - Urban drainage simulation
KW - Graph Neural Network
KW - Surrogate model
N2 - <p>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" &lt;https://doi.org/10.1016/j.watres.2024.122396&gt;.</p><p><br></p><p>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.</p><p><br></p><p>This work is supported by the TU Delft AI Labs programme.</p><p><br></p><p>This repository was supported by the Digital Competence Centre, Delft University of Technology.</p>
ER -