Analyzing Spatio-Temporal Water Quality Dynamics for the River Thames using Superstatistical Methods and Machine Learning

Analyzing Spatio-Temporal Water Quality Dynamics for the River Thames using Superstatistical Methods and Machine Learning | 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 Analyzing Spatio-Temporal Water Quality Dynamics for the River Thames using Superstatistical Methods and Machine Learning Hankun He, Takuya Boehringer, Benjamin Schäfer, Kate Heppell, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4654354/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract By employing superstatistical methods and machine learning we analyse measured time series of water quality indicators of the River Thames. Particular emphasis is on the dynamics of dissolved oxygen. The probability density functions (PDFs) of dissolved oxygen, with trend substracted, exhibit heavy tails that are well-fitted by q-Gaussian distributions. We investigate how the entropic index q depends on the distance to the sea. Regression analysis reveals feature importances for oxygen concentration predictions, with temperature, pH, and time of the year playing a major role. We also forecast oxygen concentrations through the application of a state-of-the-art machine learning model, the Transformer. Within this context, the Informer model exhibits superior performance. The effectiveness of the Informer model is attributed to the self-attention mechanism, which emphasizes the half-life cycle of dissolved oxygen and its temporal dynamics during periods from morning to early afternoon and from late evening to early morning. Our research can help to inform policymakers in ecological health assessments, assisting in river water quality forecasting and helping to maintain healthy aquatic ecosystems. Physical sciences/Physics/Statistical physics thermodynamics and nonlinear dynamics Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Statistics Earth and environmental sciences/Hydrology Earth and environmental sciences/Limnology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviews received at journal 23 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 11 Jul, 2024 Reviewers invited by journal 11 Jul, 2024 Editor assigned by journal 10 Jul, 2024 Editor invited by journal 02 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 28 Jun, 2024 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. 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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-4654354","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331079127,"identity":"9938e2a6-1a54-462e-a9be-702d7abc3cb9","order_by":0,"name":"Hankun He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACAwYeBoYPQAYfhJ9AnBbGGUAGG0lamHlI0mLOwHvwsU3ZtsQ2BuaHHxjb0ghrsWzgSzbOOXcbqIXNWIKxLYcIhx3gMZPObQNpYTBjYGyrIEqL+W9LsBb2b0RrMWNmBGvhAdlCjMMO8xhL9py7bdzGzFMskXCOCO8bHO8x/PCj7LZsP3v7xg8fypIJa2FgBhFsUEYCERqggI14paNgFIyCUTACAQABTDEbT8Q5OAAAAABJRU5ErkJggg==","orcid":"","institution":"Centre for Complex Systems, Queen Mary University of London","correspondingAuthor":true,"prefix":"","firstName":"Hankun","middleName":"","lastName":"He","suffix":""},{"id":331079128,"identity":"a85a9705-ff7f-4925-ae66-aee10d8fe12f","order_by":1,"name":"Takuya Boehringer","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Boehringer","suffix":""},{"id":331079130,"identity":"e459743d-c0a8-49ed-a011-265f7a8d31b8","order_by":2,"name":"Benjamin Schäfer","email":"","orcid":"","institution":"Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Schäfer","suffix":""},{"id":331079132,"identity":"8e458c71-2cc1-411a-a567-a2956ac2c756","order_by":3,"name":"Kate Heppell","email":"","orcid":"","institution":"Chilterns National Landscape","correspondingAuthor":false,"prefix":"","firstName":"Kate","middleName":"","lastName":"Heppell","suffix":""},{"id":331079133,"identity":"bccf91d1-8a2e-4167-949f-cfba5e069c40","order_by":4,"name":"Christian Beck","email":"","orcid":"","institution":"Centre for Complex Systems, Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Beck","suffix":""}],"badges":[],"createdAt":"2024-06-28 11:10:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4654354/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4654354/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-72084-w","type":"published","date":"2024-09-12T15:58:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64619359,"identity":"5ca408e2-f433-4e69-ad26-1a8f4e88c704","added_by":"auto","created_at":"2024-09-16 16:14:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1962018,"visible":true,"origin":"","legend":"","description":"","filename":"RiverThames.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4654354/v1_covered_3c7daa16-2b03-4308-ac73-a710ff4db92d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAnalyzing Spatio-Temporal Water Quality Dynamics for the River Thames using Superstatistical Methods and Machine Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4654354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4654354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"By employing superstatistical methods and machine learning we analyse measured time series of water quality indicators of the River Thames. 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