Reconstructing nodal pressures in water distribution systems with graph neural networks

Hajgató, Gergely; Gyires-Tóth, Bálint; Paál, György

doi:10.48550/arxiv.2104.13619

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…This leads to the task of pressure estimation at all nodes in a WDS. (Hajgató, Gyires-Tóth, and Paál 2021) used spectral GCNs to achieve encouraging results demonstrated by extensive experiments. Spectral GCNs do not fully utilize the structural information in a graph.…”

Section: Related Workmentioning

confidence: 99%

Physics-Informed Graph Neural Networks for Water Distribution Systems

Ashraf,

Strotherm,

Hermes

et al. 2024

AAAI

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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.

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Section: Related Workmentioning

confidence: 99%

Physics-Informed Graph Neural Networks for Water Distribution Systems

Ashraf,

Strotherm,

Hermes

et al. 2024

AAAI

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“…Pressure prediction errors are transformed into residual signals on the edges. A similar approach has been proposed in [17], where nodal pressures are also estimated using graph neural networks. A related approach [18] encodes data from measured nodes as images, followed by clustering to split the network into subnetworks, and a deep neural network for binary classification.…”

Section: Related Workmentioning

confidence: 99%

Wasserstein-Enabled Leaks Localization in Water Distribution Networks

Ponti,

Giordani,

Candelieri

et al. 2024

Water

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Leaks in water distribution networks are estimated to account for up to 30% of the total distributed water; moreover, the increasing demand and the skyrocketing energy cost have made leak localization and adoption ever more important to water utilities. Each leak scenario is run on a simulation model to compute the resulting values of pressure and flows over the whole network. The values recorded by the sensors are seen as features of one leak scenario and can be considered as the signature of the leak. The key distinguishing element in this paper is to consider the entire distribution of data, representing a leak as a probability distribution. In this representation, the similarity between leaks can be captured by the Wasserstein distance. This choice matches the physics of the system as follows: the equations modeling the generation of flow and pressure data are non-linear. The signatures obtained through the simulation of a set of leak scenarios are non-linearly clustered in the Wasserstein space using Wasserstein barycenters as centroids. As a new set of measurements arrives, its signature is associated with the cluster with the closest barycenter. The location of the simulated leaks belonging to that cluster are the possible locations of the observed leak. This new framework allows a richer representation of pressure and flow data embedding both the modeling and the computational modules in a space whose elements are discrete probability distribution endowed with the Wasserstein distance. Experiments on benchmark and real-world networks confirm the feasibility of the proposed approach.

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“…It was made especially popular by the work of Defferrard et al [55] and Kipf and Welling [56], who combined GNNs with CNNs to form graph convolutional networks (GCNs). This architecture has been used to optimize the performance of neural networks for many problems including traffic forecasting [57], forecasting pressure in drinking water supply networks [58], and forecasting COVID-19 infection events [59].…”

Section: Graph Neural Network (Gnns)mentioning

confidence: 99%

A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data

Burrichter

Hofmann

Silva

et al. 2023

Water

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This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation is generated to support the decision of crisis management actors. The influence of different input data, data formats, and model setups on the prediction results was investigated. Data from multiple sources were considered as follows: precipitation information, spatial information, and an overflow forecast. In addition, models with different layers and network architectures such as convolutional layers, graph convolutional layers, or generative adversarial networks (GANs) were considered and evaluated. The data required to train and test the models were generated using a coupled hydrodynamic 1D/2D model. The model setup with the inclusion of all available input variables and an architecture with graph convolutional layers presented, in general, the best results in terms of root mean square error (RMSE) and critical success index (CSI). The prediction results of the final model showed a high agreement with the simulation results of the hydrodynamic model, with drastic reductions in computation time, making this model suitable for integration into an early warning system for pluvial flooding.

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Reconstructing nodal pressures in water distribution systems with graph neural networks

Cited by 4 publications

References 30 publications

Physics-Informed Graph Neural Networks for Water Distribution Systems

Physics-Informed Graph Neural Networks for Water Distribution Systems

Wasserstein-Enabled Leaks Localization in Water Distribution Networks

A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data

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