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Water distribution systems can be modelled as graphs, being then a straightforward application of GNN. This kind of algorithm has been applied to water demand forecasting,<ref>{{cite journal |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032299|title=Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting|last=Zanfei|first=Ariele |display-authors=etal |date=2022|journal=Water Resources Research|volume=58 |issue=7 |publisher=AGU|doi=10.1029/2022WR032299 |bibcode=2022WRR....5832299Z |access-date=June 11, 2024}}</ref> interconnecting District Measuring Areas to improve the forecasting capacity. Other application of this algorithm on water distribution modelling is the development of metamodels.<ref>{{cite journal |url=https://www.sciencedirect.com/science/article/abs/pii/S0043135423007005|title=Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment|last=Zanfei|first=Ariele |journal=Water Research |display-authors=etal |date=2023|volume=242 |doi=10.1016/j.watres.2023.120264 |pmid=37393807 |bibcode=2023WatRe.24220264Z |access-date=June 11, 2024}}</ref>
=== Computer Vision ===
Moving objects detection in video surveillance is a challenging task in computer vision. Graph neural networks allow to separate background and foreground objects. Transductive <ref name="Giraldo">{{cite journal|title=Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos|journal=Fourth Workshop on Robust Subspace Learning and Computer Vision, ICCV 2021|date=October 2021|url=https://ieeexplore.ieee.org/document/9607835|last1=Giraldo|first1=H.|last2=Javed|first2=S.|last3=Werghi|first3=N.|last4=Bouwmans|first4=T.}}</ref> and inductive GNNs <ref name="Prummel">{{cite journal|title=Inductive Graph Neural Networks for Moving Object Segmentation|journal=IEEE International Conference on Image Processing, ICIP 2023|date=October 2023|url=https://arxiv.org/abs/2305.09585|last2=Prummel|first2=W.|last2=Giraldo|first2=H.|last3=Zakharova|first3=A.|last4=Bouwmans|first4=T.}}</ref>
have shown interesting performance in this task.
==References==
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