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

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;.<br>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.<br>This work is supported by the TU Delft AI Labs programme.<br>This repository was supported by the Digital Competence Centre, Delft University of Technology.

本数据集收录了代尔夫特理工大学Alexander Garzón博士项目中，针对SWMM（Storm Water Management Model，暴雨管理模型）软件开展图神经网络（Graph Neural Network）元模型开发期间采集的相关数据，对应发表论文为《基于自回归图神经网络的雨水系统节点水深可迁移且数据高效的元建模》，DOI链接：https://doi.org/10.1016/j.watres.2024.122396。
本数据集公开发布，既可作为Alexander Garzón博士论文及相关发表成果的补充数据，也可供其他研究人员在其研究工作中使用。
本研究得到代尔夫特理工大学AI实验室（TU Delft AI Labs）项目资助。
本数据集仓库得到代尔夫特理工大学数字能力中心的支持。