Using Symbolic Machine Learning to Assess and Model Substance Transport and Decay in Water Distribution Networks

Using Symbolic Machine Learning to Assess and Model Substance Transport and Decay in Water Distribution Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Using Symbolic Machine Learning to Assess and Model Substance Transport and Decay in Water Distribution Networks Daniele Biagio Laucelli, Laura Enríquez, Juan Saldarriaga, Orazio Giustolisi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3652842/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Feb, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract The evaluation of the substance concentration at each node of a water distribution network can be performed by integrating the advective diffusion in the network domain using a decay formulation and a Lagrangian scheme for differential equations. The kinetics of the substance decay can be formulated using a specific reaction order. The first order corresponds to a decay rate with a constant reaction rate, while higher order captures the reaction rate’s dependency on substance concentration. The aim of the present work was to discover the intrinsic mechanism of the substance transport in water distribution networks using the symbolic machine learning. We used the strategy named Evolutionary Polynomial Regression. To this purpose, we consider the chlorine transport, without imparing the generality of the procedure, assuming the first or second order for kinetic model. We demonstrated, using one real network and two test networks, that the concentration at each node of the network mainly depends on the substance decay along the shortest path(s) between the source and each node. Additionally, the symbolic machine learning allowed discovering the relationship between the source concentration and the residual one at each node of the network with a unique formula based on kinetic reaction model structure and its water age, possibly surrogated by travel time in the shortest path(s) to make the prediction faster. Physical sciences/Engineering Physical sciences/Engineering/Civil engineering Full Text Additional Declarations No competing interests reported. Supplementary Files SuppInfoUsingSymbolicMachineLearning.docx Cite Share Download PDF Status: Published Journal Publication published 08 Feb, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Dec, 2023 Reviews received at journal 28 Nov, 2023 Reviewers agreed at journal 28 Nov, 2023 Reviewers invited by journal 28 Nov, 2023 Editor assigned by journal 27 Nov, 2023 Editor invited by journal 24 Nov, 2023 Submission checks completed at journal 24 Nov, 2023 First submitted to journal 23 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3652842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":252790931,"identity":"cb7ea8e0-06a7-4d12-9d1b-425d37536c52","order_by":0,"name":"Daniele Biagio Laucelli","email":"","orcid":"","institution":"Polytechnic University of Bari","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"Biagio","lastName":"Laucelli","suffix":""},{"id":252790933,"identity":"bd7b622a-d3d2-4677-9f60-8f58a2d8cc62","order_by":1,"name":"Laura Enríquez","email":"","orcid":"","institution":"Polytechnic University 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