Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel’s structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems.

中文翻译：

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使用自回归图神经网络对雨水系统节点深度进行可传输和数据高效的元建模


雨水系统 （SWS） 是必不可少的基础设施，提供多种服务，包括环境保护和防洪。通常，公用事业公司依靠计算机模拟器来正确设计、操作和管理 SWS。但是，SWS 中的多个应用程序非常耗时。研究人员已经求助于运行成本较低的模型，即元模型，作为计算成本高昂的模型的替代品。随着最近人工智能应用的激增，机器学习已成为对城市供水网络进行元建模的关键方法。具体来说，深度学习方法（例如前馈神经网络）在这种情况下变得越来越重要。但是，这些方法需要生成足够大的示例数据库并训练其内部参数。这两个过程都违背了使用元模型的目的，即节省时间。为了克服这个问题，本研究的重点是应用归纳偏差和迁移学习来创建 SWS 元模型，这些元模型需要更少的数据，并且在其他地方使用时保持高性能。特别是，本研究提出了环境保护署 （EPA） 的雨水管理模型 （SWMM） 的自回归图神经网络元模型，用于估计水力水头。结果表明，与完全连接的神经网络相比，所提出的元模型需要较少数量的样本才能达到高精度和加速。此外，该元模型显示出可转移性，因为它可以用于高精度地预测网络中看不见的部分的水力水头。这项工作提出了一种新颖的方法，使城市排水从业者和水网建模研究人员都受益。 所提出的元模型可以通过为优化和蒙特卡洛分析等计算密集型任务提供高效的 SWMM 元模型，帮助从业者规划、操作和维护他们的系统。研究人员可以利用当前元模型的结构来开发针对其特定需求量身定制的新代理模型架构，或者开始为城市排水系统的更通用的基础元模型铺平道路。