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Updated: Jun 18, 2025

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基于图形神经网络的替代模型,用于城市排水网络的实时液压预测

Zhiyu Zhang1, Wenchong Tian2, Chenkaixiang Lu3

  • 1College of Environmental Science and Engineering, Tongji University, 200092, Shanghai, China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 200092, Shanghai, China.

Water research
|August 2, 2024
PubMed
概括

本研究介绍了城市排水网络的图形神经网络 (GNN) 替代模型,比传统基于物理的模型提供更快,更准确的液压预测. 物理引导的机制提高了实时应用的准确性和可解释性.

关键词:
图表神经网络的神经网络液压预测预测 液压预测替代模型的建模城市排水网络城市排水网络

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