Volcano Monitoring System for Long-Term Eruption Forecasting Using Multiple Data Sources

Volcano Monitoring System for Long-Term Eruption Forecasting Using Multiple Data Sources | 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 Volcano Monitoring System for Long-Term Eruption Forecasting Using Multiple Data Sources Takeshi Tsuji, Minoru Ideno This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5787009/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurately forecasting volcanic eruptions is challenging due to the complexity of precursory signals. Here, we develop a machine learning-based long-term eruption forecasting model for Mount Aso, Japan, by integrating multiple observational datasets—seismic tremors, magnetic field, crater wall temperature, thermal pool temperature and volume, tilt, and volcanic gas amount—at the characteristic temporal scales of the underlying physical phenomena. The temporal scales are aligned with the intrinsic dynamics captured by each dataset to enhance the model's predictive capability. We construct a theoretical framework to quantify the predictive performance improvement. Our proposed model significantly improves predictive performance, increasing the Matthews correlation coefficient by 0.65 compared to the conventional seismic-tremor-based model, and achieving a precision of >70% in predicting volcanic eruptions. Our findings demonstrate that an ensemble of multiple data sources over optimized temporal scales, underpinned by a theoretical ensemble framework, enables high-precision, interpretable eruption forecasts months in advance and makes effective disaster mitigation planning possible. Earth and environmental sciences/Solid Earth sciences/Geophysics Earth and environmental sciences/Solid Earth sciences/Volcanology Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted 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-5787009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400528694,"identity":"7dc186d3-1e61-44a5-ad44-b342eb22dadc","order_by":0,"name":"Takeshi Tsuji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYJACg4QKJJ4ElGbGpZwHrOUMXI0BcVoYGNuwaMEJ7PnPGBQ8nFeXOL+B/wAzD8MfOckZyQcYftQwsJvjskUix8AgcdvhxA0HmBmAWgyMpSXSEhh7jjEwWzbg0sID0nIgcQPQYcy8/wwS5wENYeBtYGA2OIBDC9BhBolzQA6D2ALWwvgXnxYGkMMamBMboA5LnA3UwozXlhtpBQYJxw4bbzjMbHBwDoOxsWTPs4TDMsckcPqFvf/wNsMfNXWy89sbHz54wyAnJ3E8+eDDNzU2ybhCDAjYDICEI9ArDBCXCCSAGBLJBri1MD8AEvYIPj9Epx0eLaNgFIyCUTCyAACckU2z2rHlKwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0951-4596","institution":"The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Tsuji","suffix":""},{"id":400528696,"identity":"4b86f4b2-f842-42dd-8512-6a2f62ef6b36","order_by":1,"name":"Minoru Ideno","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Minoru","middleName":"","lastName":"Ideno","suffix":""}],"badges":[],"createdAt":"2025-01-08 08:20:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5787009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5787009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781472,"identity":"cebfd188-bad2-471d-ac31-80412a615aef","added_by":"auto","created_at":"2026-03-17 07:55:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1998158,"visible":true,"origin":"","legend":"Article File","description":"","filename":"ManuscriptMtAsofinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5787009/v1_covered_8dbc22e1-7438-4f3b-beee-4561272c7f5f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Volcano Monitoring System for Long-Term Eruption Forecasting Using Multiple Data Sources","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5787009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5787009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately forecasting volcanic eruptions is challenging due to the complexity of precursory signals. 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