Feature selection for multi-parameter machine learning-driven surrogate modeling of real-time seismic intensity prediction

Feature selection for multi-parameter machine learning-driven surrogate modeling of real-time seismic intensity prediction | 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 Feature selection for multi-parameter machine learning-driven surrogate modeling of real-time seismic intensity prediction Hui Zhang, Jiaolei Zhang, Yuhong ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8101304/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract A real-time estimation of seismic intensity provides a critical basis for earthquake early warning (EEW) and emergency response. Most current methods estimate seismic intensity by relying only on one or a few parameters extracted from the P-wave without systematic feature selection, potentially neglecting essential characteristics from the ground motion (GM) records, such as amplitude and frequency-domain features. To address this issue, a multi-parameter feature selection method for machine learning-driven surrogate modeling is proposed to predict seismic intensity based on GM records obtained from the Kyoshine Network (K-Net) from 2010 to 2018. A sorting importance technique is proposed to assess 24 candidate parameters to eliminate redundancy and identify an optimal parameter combination, enhancing both model accuracy and computational efficiency. To validate the accuracy and generalization ability of the proposed method, earthquakes occurring between 2019 and 2021 are assumed to be new events and used as a verification case dataset. The results demonstrate that the proposed method effectively eliminates redundant input parameters without sacrificing accuracy, achieving an accuracy rate above 90%. Additionally, the efficiency of the optimal models in calculating input parameters, model training, and prediction is improved by factors of two, ten, and two, respectively, compared to models trained using all alternative input parameters. Finally, an actual seismic event is selected and three earthquake early warning decision-making threshold is set to further test the models. These results highlight the promising potential of the proposed method for predicting seismic intensity during emergency response in real time. Feature selection Real-time seismic intensity Earthquake early warning Multiple parameter selection machine learning-driven surrogate model Full Text Additional Declarations No competing interests reported. Supplementary Files SupplymentMaterial.rar Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 12 Nov, 2025 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-8101304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558214406,"identity":"5cfc0790-64aa-4936-a801-2347885829ef","order_by":0,"name":"Hui Zhang","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":558214407,"identity":"2d0fb2ea-0760-4da8-9db0-13074491ab09","order_by":1,"name":"Jiaolei Zhang","email":"","orcid":"","institution":"Dalian University of 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Most current methods estimate seismic intensity by relying only on one or a few parameters extracted from the P-wave without systematic feature selection, potentially neglecting essential characteristics from the ground motion (GM) records, such as amplitude and frequency-domain features. To address this issue, a multi-parameter feature selection method for machine learning-driven surrogate modeling is proposed to predict seismic intensity based on GM records obtained from the Kyoshine Network (K-Net) from 2010 to 2018. A sorting importance technique is proposed to assess 24 candidate parameters to eliminate redundancy and identify an optimal parameter combination, enhancing both model accuracy and computational efficiency. To validate the accuracy and generalization ability of the proposed method, earthquakes occurring between 2019 and 2021 are assumed to be new events and used as a verification case dataset. The results demonstrate that the proposed method effectively eliminates redundant input parameters without sacrificing accuracy, achieving an accuracy rate above 90%. Additionally, the efficiency of the optimal models in calculating input parameters, model training, and prediction is improved by factors of two, ten, and two, respectively, compared to models trained using all alternative input parameters. Finally, an actual seismic event is selected and three earthquake early warning decision-making threshold is set to further test the models. 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