Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge

Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge | 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 Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge Gabriel Spadon, Ruixin Song, Ronald Pelot, Stan Matwin, Amilcar Soares This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3917933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Aquatic invasive species are a significant threat to biodiversity and ecosystems worldwide. Due to the fast growth of global trade and transportation networks, non-native species have been introduced and spread unintentionally in new environments. These invasive species can damage ecosystems by disrupting the balance, out-competing native species, and causing significant economic losses to agriculture, forestry, and fisheries. Therefore, there is a pressing need for risk assessment and management techniques to mitigate the impact of these invasions. This study aims to develop a new physics-inspired model to forecast maritime shipping traffic and thus inform risk assessment of invasive species spread through global transportation networks. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Additionally, by analyzing the risk network of invasive species, we provide a comprehensive framework for assessing the invasion threat level given a pair of origin and destination. Accordingly, in this paper, we introduce transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% segmentation accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of invasive species risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing species invasion risks in a changing global landscape. Physical sciences/Physics/Applied physics Earth and environmental sciences/Ocean sciences Earth and environmental sciences/Ecology/Invasive species Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Apr, 2024 Reviews received at journal 07 Mar, 2024 Reviewers agreed at journal 28 Feb, 2024 Reviewers agreed at journal 18 Feb, 2024 Reviewers invited by journal 16 Feb, 2024 Editor assigned by journal 16 Feb, 2024 Editor invited by journal 09 Feb, 2024 Submission checks completed at journal 09 Feb, 2024 First submitted to journal 01 Feb, 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. 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-3917933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272026276,"identity":"3fe60a62-7f3c-4103-9c54-612d2f489dd1","order_by":0,"name":"Gabriel Spadon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFACHgaGBAYLORBTAirERowWCWMStQAVJzYQrUXe/eyxDw8qJNLnz25+eOPjDgZ5frEDbI8r8GgxPJOXPCPhjETuhjvHjC1nnmEwnDk7gd3wDD4tDTnGDIltQC0SCWbSvG0MCQa3E9gkG/Bp6X8D1PJPIl1+Rvo34rTIS4BsaZBIYLiRQ6QtBhLvkhkSjkkYbrhzpthyZpsE0C+J7YZ4benPPcz4o8ZGXn52+8YbH9ts5Pmlk489xGvLARhLAk4y4tMAtAUuLYFH1SgYBaNgFIxsAACIzEiTgECTDwAAAABJRU5ErkJggg==","orcid":"","institution":"Dalhousie University","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"","lastName":"Spadon","suffix":""},{"id":272026277,"identity":"f1835e87-e115-4690-80b0-8810d6549cfe","order_by":1,"name":"Ruixin Song","email":"","orcid":"","institution":"Memorial University","correspondingAuthor":false,"prefix":"","firstName":"Ruixin","middleName":"","lastName":"Song","suffix":""},{"id":272026278,"identity":"dca2a206-218b-4d87-9657-56b7122b8f31","order_by":2,"name":"Ronald Pelot","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"","lastName":"Pelot","suffix":""},{"id":272026279,"identity":"9a76065a-329e-42fc-a75a-4fcd5ccd042d","order_by":3,"name":"Stan Matwin","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Stan","middleName":"","lastName":"Matwin","suffix":""},{"id":272026280,"identity":"20deabcb-a918-40a7-ac6d-2babbf200b75","order_by":4,"name":"Amilcar Soares","email":"","orcid":"","institution":"Linnaeus University","correspondingAuthor":false,"prefix":"","firstName":"Amilcar","middleName":"","lastName":"Soares","suffix":""}],"badges":[],"createdAt":"2024-02-01 15:44:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3917933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3917933/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-67552-2","type":"published","date":"2024-07-19T16:13:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61595201,"identity":"99ae66be-f6d7-40ee-ab16-387f0f3f99c2","added_by":"auto","created_at":"2024-08-01 17:20:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4170443,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3917933/v1_covered_17d366d1-c85b-48c8-8ebf-3bc5bc76bfdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge","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-3917933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3917933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Aquatic invasive species are a significant threat to biodiversity and ecosystems worldwide. Due to the fast growth of global trade and transportation networks, non-native species have been introduced and spread unintentionally in new environments. These invasive species can damage ecosystems by disrupting the balance, out-competing native species, and causing significant economic losses to agriculture, forestry, and fisheries. Therefore, there is a pressing need for risk assessment and management techniques to mitigate the impact of these invasions. This study aims to develop a new physics-inspired model to forecast maritime shipping traffic and thus inform risk assessment of invasive species spread through global transportation networks. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. 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