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使用自动回归图神经网络进行雨水系统节点深度的可转移和数据高效的元模型
Alexander Garzón1, Zoran Kapelan1, Jeroen Langeveld2
1Delft University of Technology, Stevinweg 1, Delft, 2628 CN, The Netherlands.
Water research
|September 14, 2024
概括
本研究引入了用于雨水系统 (SWSs) 的自动回归图神经网络元模型,显著减少了数据需求,并提高了城市排水管理的计算效率.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 城市水资源管理
背景情况:
- 雨水系统 (SWS) 是关键基础设施,需要复杂的模拟来设计和运行.
- 传统的SWS模型在计算上昂贵,这导致使用元模型提高效率.
- 机器学习,特别是深度学习,越来越多地用于SWS元模型,但通常需要大量的数据和培训时间.
研究的目的:
- 为雨水系统开发一种新,数据效率高,可转移的元模型.
- 在数据要求和培训时间方面克服传统深度学习元模型的局限性.
- 为环境保护局的风暴水管理模型 (SWMM) 创建一个自动回归图神经网络元模型.
主要方法:
- 归纳偏差和转移学习用于SWS元模型的应用.
- 开发一个自动回归图形神经网络元模型.
- 使用风暴水管理模型 (SWMM) 来估计液压头部.
主要成果:
- 与完全连接的神经网络相比,拟议的元模型在训练示例数量减少的情况下实现了高精度.
- 观察到计算中的显著加快速度.
- 超级模型在预测网络未见部分的液压头方面表现出高准确性,这表明其具有很强的可转移性.
结论:
- 开发的自动回归图神经网络元模型为SWS模拟提供了更有效的替代方案.
- 这种方法减少了数据依赖,并提高了城市水资源管理中元模型的适用性.
- 这些发现使实践者受益,使规划和维护速度更快,研究人员通过为先进的代孕模型技术提供基础.
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