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评估图形神经网络用于城市排水元模型:关键组件和可转移性分析
Alexander Garzón1, Zoran Kapelan1, Jeroen Langeveld2
1Delft University of Technology, Stevinweg 1, Delft, 2628 CN, The Netherlands.
Water research
|December 12, 2025
概括
图形神经网络 (GNN) 提供快速的城市排水模拟. 优化GNN架构和转移学习可以提高液压建模任务的准确性和通用性.
科学领域:
- 环境工程 环境工程
- 计算式水力学 计算式水力学
- 水资源中的人工智能
背景情况:
- 城市排水液压模拟是计算密集的,阻碍了实时应用.
- 图形神经网络 (GNN) 作为元模型具有前景,但其架构影响和可转移性尚未完全理解.
研究的目的:
- 系统地评估城市排水液压的GNN元模型组件.
- 评估GNN元模型在不同排水网络和预测任务中的可转移性.
主要方法:
- 对两个不同的荷兰综合下水道网络进行了案例研究.
- 评估的GNN架构组件:图层类型,处理器深度和预测窗口.
- 进行了可转移性实验,包括域和任务适应.
主要成果:
- 具有中度深度和10步预测的元模型实现了高精度 (头部RMSE 2-5厘米,流量为0.02 m3/s).
- 在GPU上,GNN提供了高达四个数量级的加速度,比GPU上的SWMM快.
- 在域内实现了有效的转移学习;跨域转移需要规范化和微调.
结论:
- GNN元模型架构可以反映物理运输动态.
- 优化的GNN为快速,准确和可泛化的城市排水建模提供了实用的基础.
- 结果支持GNN用于诸如不确定性分析,设计优化和现在预测等应用.
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