Granular Edge Intelligence for Early Earthquake Detection Using Sand-Based Sensing and Hybrid Deep Learning with Human Impact Analysis

Granular Edge Intelligence for Early Earthquake Detection Using Sand-Based Sensing and Hybrid Deep Learning with Human Impact Analysis | 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 Research Article Granular Edge Intelligence for Early Earthquake Detection Using Sand-Based Sensing and Hybrid Deep Learning with Human Impact Analysis K.Gayathri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8922323/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 Earthquakes are among the most devastating natural disasters, causing widespread destruction to infrastructure and posing significant threats to human life. Early warning systems are essential to reduce fatalities and enable effective disaster management. Traditional seismic monitoring systems, while valuable, are often limited by their reliance on fixed sensor placements, high costs, and inability to integrate with real-time human identification in affected areas. Additionally, environmental factors such as heavy air pollution can impair visibility and facial recognition capabilities, hindering post-disaster rescue operations. Research presents an advanced Granular Edge Intelligence Framework that integrates sand-based geotechnical sensing, IoT-enabled real-time data acquisition, and hybrid deep learning models for early earthquake detection. The framework further incorporates facial recognition techniques to identify trapped or injured individuals within collapsed buildings, even under conditions of dense particulate matter. Utilizing CNN-LSTM architectures, the system processes multivariate sensor data and video streams Simultaneously, providing high-accuracy earthquake prediction and rapid human identification. Extensive simulations and experimental evaluations demonstrate 95.2% accuracy in earthquake detection and robust human identification performance under heavy pollution, indicating the framework’s applicability for deployment in urban disaster-prone environments Artificial Intelligence and Machine Learning Granular edge intelligence earthquake detection sand-based sensing IoT hybrid deep learning facial recognition heavy air pollution Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8922323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594226321,"identity":"7b772020-add8-4e38-917d-7e113fe67d56","order_by":0,"name":"K.Gayathri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgYYix3EMrAgRQvPAZAWCVLsk0gAk4QVMrf3PvvM88succPN51c3/CiQYOBv707A77Ce48azefuSEzfczim72QN0mMSZsxvwa5mRxszM28MM0pJ2gweoxUAil4CW+c9AWuqBDjuTdvMPUVpmsDEz8/w4nLjhBvux28TZ0pPGzDi34bjxzDM5bLdlDCR4CPrFsP0YM8ObP9WyfcePP7v55o+NHH97LwEtDQwMTLxtDI4NDDwGIAEevMpBQB7kuB9/GOyBKeYBQdWjYBSMglEwMgEA64JLEE39oQUAAAAASUVORK5CYII=","orcid":"","institution":"Anna University Chennai - Regional Campus Madurai","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"K.Gayathri","suffix":""}],"badges":[],"createdAt":"2026-02-20 04:46:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8922323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8922323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103287881,"identity":"87341cf7-156d-48d1-880f-d495b24c78ad","added_by":"auto","created_at":"2026-02-24 05:25:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":790155,"visible":true,"origin":"","legend":"","description":"","filename":"Transactionstemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8922323/v1_covered_85c7b628-5b2d-4fcf-8bcc-1ca5991ec03d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGranular Edge Intelligence for Early Earthquake Detection Using Sand-Based Sensing and Hybrid Deep Learning with Human Impact Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Anna University, Chennai","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":"Granular edge intelligence, earthquake detection, sand-based sensing, IoT, hybrid deep learning, facial recognition, heavy air pollution","lastPublishedDoi":"10.21203/rs.3.rs-8922323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8922323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarthquakes are among the most devastating natural disasters, causing widespread destruction to infrastructure and posing significant threats to human life. Early warning systems are essential to reduce fatalities and enable effective disaster management. Traditional seismic monitoring systems, while valuable, are often limited by their reliance on fixed sensor placements, high costs, and inability to integrate with real-time human identification in affected areas.\u003c/p\u003e\n\u003cp\u003eAdditionally, environmental factors such as heavy air pollution can impair visibility and facial recognition capabilities, hindering post-disaster rescue operations.\u003c/p\u003e\n\u003cp\u003eResearch presents an advanced Granular Edge Intelligence Framework that integrates sand-based geotechnical sensing, IoT-enabled real-time data acquisition, and hybrid deep learning models for early earthquake detection.\u003c/p\u003e\n\u003cp\u003eThe framework further incorporates facial recognition techniques to identify trapped or injured individuals within collapsed buildings, even under conditions of dense particulate matter. 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