{"paper_id":"071dcd1d-541c-432d-a5b2-ec8965514ba0","body_text":"Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy | 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 Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy Luca Patelli, Michela Cameletti, Valerio De Rubeis, Nicola Alessandro Pino, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7601879/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract After an earthquake, it is crucial to rapidly and accurately estimate macroseismic intensity to guide rescue operations and assess potential damage. The Mercalli-Cancani-Sieberg intensity scale is used to qualitatively assess the ground shaking based on observed effects. This study develops a Machine Learning framework, leveraging the Random Forest algorithm, to estimate macroseismic intensity using early available seismic data. Data from different sources are used for model training: seismic data from the Italian instrumental monitoring networks of Istituto Nazionale di Geofisica e Vulcanologia and Protezione Civile , as well as macroseismic intensity data from both the online macroseismic questionnaire and the on-site surveys by field experts. In order to explain the predictive mechanism of the Random Forest algorithm, this study makes use of surrogate decision trees, providing an interpretative key for the informed decision-making process during seismic events. These models provide insights into the relationships between covariates and predicted intensities, enabling the discussion of model complexity, predictive capability, and explainability. Furthermore, the uncertainty associated with the predictions of the surrogate trees is assessed. When compared with other models for estimating intensity based on ground motion peaks or source parameters, the Random Forest model achieved better predictive performance. Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Earthquake Random Forest Surrogate tree Macroseismic intensity Explainability Uncertainty quantification Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationMachineLearningforPromptEstimationofMacroseismicIntensityfromSeismometricDatainItaly.docx Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Editor invited by journal 19 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 12 Sep, 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. 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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-7601879\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":527922469,\"identity\":\"34d213d5-3e64-4d42-ba1a-8918282f8ffb\",\"order_by\":0,\"name\":\"Luca Patelli\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Bergamo\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luca\",\"middleName\":\"\",\"lastName\":\"Patelli\",\"suffix\":\"\"},{\"id\":527922470,\"identity\":\"2d5df5c8-f884-44b4-80dc-07126aa02bdc\",\"order_by\":1,\"name\":\"Michela 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Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Earthquake, Random Forest, Surrogate tree, Macroseismic intensity, Explainability, Uncertainty quantification\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7601879/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7601879/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAfter an earthquake, it is crucial to rapidly and accurately estimate macroseismic intensity to guide rescue operations and assess potential damage. The Mercalli-Cancani-Sieberg intensity scale is used to qualitatively assess the ground shaking based on observed effects. This study develops a Machine Learning framework, leveraging the Random Forest algorithm, to estimate macroseismic intensity using early available seismic data. Data from different sources are used for model training: seismic data from the Italian instrumental monitoring networks of \\u003cem\\u003eIstituto Nazionale di Geofisica e Vulcanologia\\u003c/em\\u003e and \\u003cem\\u003eProtezione Civile\\u003c/em\\u003e, as well as macroseismic intensity data from both the online macroseismic questionnaire and the on-site surveys by field experts. In order to explain the predictive mechanism of the Random Forest algorithm, this study makes use of surrogate decision trees, providing an interpretative key for the informed decision-making process during seismic events. These models provide insights into the relationships between covariates and predicted intensities, enabling the discussion of model complexity, predictive capability, and explainability. Furthermore, the uncertainty associated with the predictions of the surrogate trees is assessed. When compared with other models for estimating intensity based on ground motion peaks or source parameters, the Random Forest model achieved better predictive performance.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-14 10:46:23\",\"doi\":\"10.21203/rs.3.rs-7601879/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-28T07:21:15+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-27T20:07:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-27T10:45:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"35416567218288553473186928548806521238\",\"date\":\"2025-10-27T08:37:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"329297140403439700558657288859674054089\",\"date\":\"2025-10-25T20:51:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"217320841594078982285836181761110288661\",\"date\":\"2025-10-06T09:52:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"25230326952675561173387043192407132542\",\"date\":\"2025-10-03T09:28:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-01T02:59:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-19T15:28:30+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-15T08:15:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-13T11:37:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-09-12T15:09:01+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"b7107cf9-4639-453f-84f0-82443adfdcc3\",\"owner\":[],\"postedDate\":\"October 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":56119706,\"name\":\"Physical sciences/Mathematics and computing\"},{\"id\":56119707,\"name\":\"Earth and environmental sciences/Natural hazards\"},{\"id\":56119708,\"name\":\"Earth and environmental sciences/Solid earth sciences\"}],\"tags\":[],\"updatedAt\":\"2026-02-09T16:05:25+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7601879\",\"link\":\"https://doi.org/10.1038/s41598-026-35740-x\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2026-02-04 15:57:38\",\"publishedOnDateReadable\":\"February 4th, 2026\"},\"versionCreatedAt\":\"2025-10-14 10:46:23\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-026-35740-x\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-026-35740-x\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7601879\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7601879\",\"identity\":\"rs-7601879\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}