Accelerating Urban Drainage Simulations: A Data-Efficient GNN Metamodel for SWMM Flowrates

Computational models for water resources often experience slow execution times, limiting their application. Metamodels, especially those based on machine learning, offer a promising alternative. Our research extends a prior Graph Neural Network (GNN) metamodel for the Storm Water Management Model (S...

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書誌詳細

主要な著者:	Alexander Garzón, Zoran Kapelan, Jeroen Langeveld, Riccardo Taormina
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2024-09-01
シリーズ:	Engineering Proceedings
主題:	surrogate modeling storm water drainage artificial intelligence data efficiency zero-shot learning
オンライン･アクセス:	https://www.mdpi.com/2673-4591/69/1/137
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