Physics-Informed Graph Neural Networks for Water Distribution Systems
Inaam Ashraf Janine Strotherm Luca Hermes Barbara Hammer
Water distribution systems (WDS) are an integral part of critical
infrastructure which is pivotal to urban development. As 70% of the world's
population will likely live in urban environments in 2050, efficient simulation
and planning tools for WDS play a crucial role in reaching UN's sustainable
developmental goal (SDG) 6 - "Clean water and sanitation for all". In this
realm, we propose a novel and efficient machine learning emulator, more
precisely, a physics-informed deep learning (DL) model, for hydraulic state
estimation in WDS. Using a recursive approach, our model only needs a few graph
convolutional neural network (GCN) layers and employs an innovative algorithm
based on message passing. Unlike conventional machine learning tasks, the model
uses hydraulic principles to infer two additional hydraulic state features in
the process of reconstructing the available ground truth feature in an
unsupervised manner. To the best of our knowledge, this is the first DL
approach to emulate the popular hydraulic simulator EPANET, utilizing no
additional information. Like most DL models and unlike the hydraulic simulator,
our model demonstrates vastly faster emulation times that do not increase
drastically with the size of the WDS. Moreover, we achieve high accuracy on the
ground truth and very similar results compared to the hydraulic simulator as
demonstrated through experiments on five real-world WDS datasets.