Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

中文翻译：

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图卷积网络：在废水网络数据库完成中的应用

污水管网是城市化所必需的。他们的管理，包括维修和扩建操作的预测和规划，需要有关其地下组件（井盖、设备、节点和管道）的精确信息。然而，由于它们的服务年限以及随着时间的推移它们可能经历的维护操作数量不断增加，与构成网络的各种对象相关联的属性和特征在给定时间并非全部可用。这部分是因为 (i) 进行维修和扩展的多个参与者不一定是确保网络持续运行的运营商，以及 (ii) 未正确跟踪和报告所进行的更改。因此，与废水网络相关的数据库可能会丢失数据。为了克服这个问题，我们的目标是在机器学习方法的学习过程中利用废水网络的结构，使用拓扑和组件之间的关系来完成管道的缺失值。我们的结果表明，图卷积网络 (GCN) 模型比经典方法产生更好的结果，并且代表了缺失数据补全的有用工具。