The Avalanche Risk Prediction Intelligent System: Susa Valley Alps Case Study

The Avalanche Risk Prediction Intelligent System: Susa Valley Alps Case Study | 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 Case Report The Avalanche Risk Prediction Intelligent System: Susa Valley Alps Case Study Hoang Minh Bui, Salvatore Distefano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7766100/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Snow avalanches pose a significant and escalating natural hazard, especially in climate change and global warming times, demanding advanced predictive capabilities to safeguard lives and infrastructure in mountainous regions. Traditional methods, even when augmented by machine learning applications, frequently contend with inherent data complexities, imbalanced event occurrences, and a critical trade-off between minimizing false negative predictions (safety risks) and reducing economically detrimental false positives in a timely manner. This paper introduces the Avalanche Risk Prediction Intelligent System (ARPIS) implementing a novel data-driven approach designed to address these challenges through a comprehensive, machine learning-centric framework. ARPIS proposes an incremental, feedback-oriented methodology that continuously collects and manages data, improves the model, and infers live avalanche risk prediction, eventually triggering alerts to support decision making. The ARPIS suitability and effectiveness are demonstrated through a full-fledged case study on a dedicated dataset from the high-risk mountainous terrain of the Susa Valley in the Italian Alps, which code and dataset are publicly available. Avalanche Prediction Risk Management Natural Hazards Intelligent Systems Machine Learning Data-driven Pipeline Operational Framework Susa Valley Alps. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 04 Oct, 2025 First submitted to journal 02 Oct, 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. 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-7766100","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":559999943,"identity":"7779457b-e732-4e8b-a36a-afc45df3a7b9","order_by":0,"name":"Hoang Minh Bui","email":"","orcid":"","institution":"University of Messina","correspondingAuthor":false,"prefix":"","firstName":"Hoang","middleName":"Minh","lastName":"Bui","suffix":""},{"id":559999945,"identity":"ee9984bb-f6b3-4a29-a255-e327c0372492","order_by":1,"name":"Salvatore Distefano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFCCxAYgIQfEzAcYGAxANDFaDjAYAxlsCVAtBPUAFUK08BhARQhokW9Pbv78ocJA3py955t0QcE2OQZ2/gN4tRicedgmceCMgeHOnrPbpGcY3DYm6DADicQ2hoNtfxg33MjdJs1jcDuxgZAW+RmJzR8O/jOw33Aj5xlISz1BLQw3EhskDjYYJAK1sIG0JBB2GMgvZ44ZJG84c8zYGqjFsI2Z2QCvFvn29McfKmoMbDccb354m+fPbXl+/oMPCLgMHbCRqH4UjIJRMApGARYAAMAwRgKOtvAuAAAAAElFTkSuQmCC","orcid":"","institution":"University of Messina","correspondingAuthor":true,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Distefano","suffix":""}],"badges":[],"createdAt":"2025-10-02 11:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7766100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7766100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98194455,"identity":"111f1f9d-4c26-4815-a151-7e36736f923e","added_by":"auto","created_at":"2025-12-15 06:25:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2346057,"visible":true,"origin":"","legend":"","description":"","filename":"AVANLANCHEPAPER.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7766100/v1/5cea55c74b8b6eeb371762ff.pdf"},{"id":98194452,"identity":"91c18a9c-d54b-47d3-8c95-91b458b1e581","added_by":"auto","created_at":"2025-12-15 06:25:22","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4013,"visible":true,"origin":"","legend":"","description":"","filename":"e7075cfa5c4d44a0b614efb8e2932470.json","url":"https://assets-eu.researchsquare.com/files/rs-7766100/v1/89b4ee8f4a29f992e9415ce7.json"},{"id":98431769,"identity":"b0250a24-9364-462f-8316-afb5ba561b28","added_by":"auto","created_at":"2025-12-17 16:48:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658385,"visible":true,"origin":"","legend":"","description":"","filename":"AVANLANCHEPAPER.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7766100/v1_covered_ddbf07f3-260f-4719-8e95-cb517bb8ff04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Avalanche Risk Prediction Intelligent System: Susa Valley Alps Case Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-big-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigd","sideBox":"Learn more about [Journal of Big Data](http://journalofbigdata.springeropen.com)","snPcode":"40537","submissionUrl":"https://submission.nature.com/new-submission/40537/3","title":"Journal of Big Data","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Avalanche Prediction, Risk Management, Natural Hazards, Intelligent Systems, Machine Learning, Data-driven Pipeline, Operational Framework, Susa Valley, Alps.","lastPublishedDoi":"10.21203/rs.3.rs-7766100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7766100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Snow avalanches pose a significant and escalating natural hazard, especially in climate change and global warming times, demanding advanced predictive capabilities to safeguard lives and infrastructure in mountainous regions. Traditional\nmethods, even when augmented by machine learning applications, frequently\ncontend with inherent data complexities, imbalanced event occurrences, and\na critical trade-off between minimizing false negative predictions (safety risks)\nand reducing economically detrimental false positives in a timely manner. This\npaper introduces the Avalanche Risk Prediction Intelligent System (ARPIS)\nimplementing a novel data-driven approach designed to address these challenges\nthrough a comprehensive, machine learning-centric framework. ARPIS proposes\nan incremental, feedback-oriented methodology that continuously collects and\nmanages data, improves the model, and infers live avalanche risk prediction, eventually triggering alerts to support decision making. The ARPIS suitability and effectiveness are demonstrated through a full-fledged case study on a dedicated dataset from the high-risk mountainous terrain of the Susa Valley in the Italian Alps, which code and dataset are publicly available.","manuscriptTitle":"The Avalanche Risk Prediction Intelligent System: Susa Valley Alps Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 06:24:45","doi":"10.21203/rs.3.rs-7766100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-28T15:51:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T22:55:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T08:11:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T05:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291912730298581606171807218543505225335","date":"2025-12-11T05:16:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T10:54:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191619321846008544153342575714977710793","date":"2025-12-10T06:01:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254034584931316516662988606011275684583","date":"2025-12-10T03:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7884922257460121412024764808799881088","date":"2025-12-10T02:57:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124842010319686742748146564138034589931","date":"2025-12-10T02:55:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T02:29:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T22:40:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-04T13:00:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Big Data","date":"2025-10-02T11:25:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-big-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigd","sideBox":"Learn more about [Journal of Big Data](http://journalofbigdata.springeropen.com)","snPcode":"40537","submissionUrl":"https://submission.nature.com/new-submission/40537/3","title":"Journal of Big Data","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e6b8d74-7284-4645-abe0-fbdcce2fddf9","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T18:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 06:24:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7766100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7766100","identity":"rs-7766100","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}