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Showing content from https://pubmed.ncbi.nlm.nih.gov/37393807 below:

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

. 2023 Aug 15:242:120264. doi: 10.1016/j.watres.2023.120264. Epub 2023 Jun 24. Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

Affiliations

¹ AIAQUA S.r.l., Via Volta 13/A, Bolzano, Italy. Electronic address: a.zanfei@aiaqua.tech.
² Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, Bolzano, Italy. Electronic address: andrea.menapace@unibz.it.
³ Hydraulic Engineering and Water Resources Department, School of Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil. Electronic address: brentan@ehr.ufmg.br.
⁴ Unit of Environmental Engineering, Department of Infrastructure Engineering - University of Innsbruck, Techniker Str. 13, 6020 Innsbruck, Austria. Electronic address: robert.sitzenfrei@uibk.ac.at.
⁵ Institute for Manufacturing, Department of Engineering - University of Cambridge, 17 Charles Babbage Rd, Cambridge CB3 0FS, UK. Electronic address: amh226@cam.ac.uk.

PMID: 37393807
DOI: 10.1016/j.watres.2023.120264

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Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

Ariele Zanfei et al. Water Res. 2023.

. 2023 Aug 15:242:120264. doi: 10.1016/j.watres.2023.120264. Epub 2023 Jun 24. Affiliations

¹ AIAQUA S.r.l., Via Volta 13/A, Bolzano, Italy. Electronic address: a.zanfei@aiaqua.tech.
² Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, Bolzano, Italy. Electronic address: andrea.menapace@unibz.it.
³ Hydraulic Engineering and Water Resources Department, School of Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil. Electronic address: brentan@ehr.ufmg.br.
⁴ Unit of Environmental Engineering, Department of Infrastructure Engineering - University of Innsbruck, Techniker Str. 13, 6020 Innsbruck, Austria. Electronic address: robert.sitzenfrei@uibk.ac.at.
⁵ Institute for Manufacturing, Department of Engineering - University of Cambridge, 17 Charles Babbage Rd, Cambridge CB3 0FS, UK. Electronic address: amh226@cam.ac.uk.

PMID: 37393807
DOI: 10.1016/j.watres.2023.120264

Item in Clipboard

Abstract

Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calibration process is mandatory to achieve plausible simulation results. However, calibration is affected by a set of intrinsic uncertainty sources, mainly related to the lack of system knowledge. This paper proposes a breakthrough approach for calibrating hydraulic models through a graph machine learning approach. The main idea is to create a graph neural network metamodel to estimate the network behaviour based on a limited number of monitoring sensors. Once the flows and pressures of the entire network have been estimated, a calibration is carried out to obtain the set of hydraulic parameters that best approximates the metamodel. Through this process, it is possible to estimate the uncertainty that is transferred from the few available measurements to the final hydraulic model. The paper sparks a discussion to assess under what circumstances a graph-based metamodel might be a solution for water network analysis.

Keywords: Calibration; Deep learning; Graph convolutional networks; Graph neural networks; Metamodel; Water distribution systems.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.