Forecasting earthquakes by Machine Learning techniques: lights and shadows

Forecasting earthquakes by Machine Learning techniques: lights and shadows | 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 Forecasting earthquakes by Machine Learning techniques: lights and shadows Jesús Jover-Alfaro, Enrique Arias-Antúnez, Jose Antonio Mateo-Cortés This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7590274/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 Earthquake prediction research continues to be a very active research topic because its correct prediction could save many human lives as well as the impact it has on the economy of a country or region. Furthermore, the prediction of earthquakes, both in terms of location, time, magnitude and probability also continues to be a topic of interest in research due to the difficulty of predicting them, precisely because of the high non-linearity of their behaviour.This is why Machine Learning methods are becoming increasingly popular for the challenging task of earthquake prediction. The methods presented limit their application to one geographical area, which makes sense because of the different structure of the earth's crust that greatly affects earthquakes, and with spectacular results. However, could these methods be applied to other geographical locations with good results? The results obtained, if good, could they be applied to the real behaviour of seismic phenomena? This work answers these questions and compares its results with those found in the literature, and concludes that the method chosen for the comparison does not present a correct behaviour in a real environment. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7590274","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519923870,"identity":"1b4bbadf-e709-4c1e-994f-cdc47961d8df","order_by":0,"name":"Jesús Jover-Alfaro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACxgYwxQZmP2BgsCBNC7MBA4MEaTaySRClhbm9/ZnkDwY+OYPjvc+qeWokohnY2x/gd1jPGTNpHgY2Y4Mzx81u8xyTyG3gOWOAX8uMHDZpoJsSZ85IY7vN2wDUIpGD32GM85+DHAbUMv8ZWzFYi/xzAg6bwWAmAXRYYr8EGxszxBYGAg7ryTG25jFgM+bnSWOWnAP0SxtPDn4thu3HH978UXFMjo39GOOHNzU2uf3sx/E7zLABRBocQ4iw4VUPBPIQqoaQulEwCkbBKBjJAABEAzo7OdmuzgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":true,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Jover-Alfaro","suffix":""},{"id":519923872,"identity":"11110818-372d-4a07-8897-0944c26944c8","order_by":1,"name":"Enrique Arias-Antúnez","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Arias-Antúnez","suffix":""},{"id":519923873,"identity":"9f760df8-aa65-476d-9864-2914c2a2205b","order_by":2,"name":"Jose Antonio Mateo-Cortés","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Antonio","lastName":"Mateo-Cortés","suffix":""}],"badges":[],"createdAt":"2025-09-11 09:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7590274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7590274/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92190417,"identity":"61ca0cf2-b3ac-404a-9b29-2ebc885f0de1","added_by":"auto","created_at":"2025-09-25 15:05:56","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5180,"visible":true,"origin":"","legend":"","description":"","filename":"28c2a65228c14423b6761f953f5062ed.json","url":"https://assets-eu.researchsquare.com/files/rs-7590274/v1/b39412576e15e310e13324e5.json"},{"id":94673556,"identity":"6164fae9-088f-4ce6-997f-e2d9e6307310","added_by":"auto","created_at":"2025-10-29 13:41:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32975364,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingearthquakesbyMachineLearningtechniqueslightsandshadowsv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7590274/v1_covered_383718f5-54b4-4016-882c-321ab8643b15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting earthquakes by Machine Learning techniques: lights and shadows","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-7590274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7590274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Earthquake prediction research continues to be a very active research topic because its correct prediction could save many human lives as well as the impact it has on the economy of a country or region. 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