Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of wat...

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Bibliographic Details
Main Authors: Bulat Kerimov, Roberto Bentivoglio, Alexander Garzón, Elvin Isufi, Franz Tscheikner-Gratl, David Bernhard Steffelbauer, Riccardo Taormina
Format: Article
Language:English
Published: IWA Publishing 2023-11-01
Series:Journal of Hydroinformatics
Subjects:
artificial intelligence
graph neural network
surrogate model
transfer learning
water distribution system
water network
Online Access:http://jhydro.iwaponline.com/content/25/6/2223

Internet

http://jhydro.iwaponline.com/content/25/6/2223

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