Landslide Susceptibility Mapping around Alkumru Dam (Siirt, Türkiye) Using Machine Learning and Ensemble Models

Landslide Susceptibility Mapping around Alkumru Dam (Siirt, Türkiye) Using Machine Learning and Ensemble Models | 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 Landslide Susceptibility Mapping around Alkumru Dam (Siirt, Türkiye) Using Machine Learning and Ensemble Models Muhammed Mustafa Özdel, Melike Durak, Serkan Sabancı, İsa Cürebal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9218127/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study assessed the landslide susceptibility of the Alkumru Dam Reservoir and its surrounding areas in Siirt Province, Türkiye, using six machine learning algorithms—Support Vector Machines, Logistic Regression, Maximum Entropy, Naive Bayes, Random Forest, and Artificial Neural Networks—alongside an Ensemble model. The analyses were conducted using MaxEnt and R software. A comprehensive landslide inventory and eleven conditioning factors were employed during the modeling process. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC) metric. All models demonstrated high predictive accuracy (AUC = 0.877–0.959), with the Naive Bayes model achieving the highest performance (AUC = 0.959). The spatial distribution of susceptibility maps was highly consistent across all algorithms. High and very-high-susceptibility zones were consistently identified on the southern, western, and southwestern slopes of the Alkumru Dam Reservoir and the right slope of the Botan River Valley—areas characterized by weak lithological units with low mechanical strength. Quantitative validation confirmed that 52.2%-76.8% of mapped landslides fall within high- and very-high-risk zones. The resulting susceptibility maps provide a valuable scientific basis for sustainable land-use planning, disaster risk management, and dam safety in the region. Landslide Susceptibility Mapping Machine Learning Alkumru Dam Reservoir Disaster Risk Management Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 25 Mar, 2026 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-9218127","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615470254,"identity":"1f1c0d46-b6b5-4a18-8e9d-5f51511479fc","order_by":0,"name":"Muhammed Mustafa Özdel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Mustafa","lastName":"Özdel","suffix":""},{"id":615470255,"identity":"680dd5ad-fd7e-448b-90b8-0f3e70589cba","order_by":1,"name":"Melike Durak","email":"","orcid":"","institution":"Balıkesir University","correspondingAuthor":false,"prefix":"","firstName":"Melike","middleName":"","lastName":"Durak","suffix":""},{"id":615470256,"identity":"db7a4655-77c1-45d6-a8e2-ed14deb84bcb","order_by":2,"name":"Serkan Sabancı","email":"","orcid":"","institution":"Siirt University","correspondingAuthor":false,"prefix":"","firstName":"Serkan","middleName":"","lastName":"Sabancı","suffix":""},{"id":615470257,"identity":"c215ab01-69ae-41fe-93b3-937ee9717648","order_by":3,"name":"İsa Cürebal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYJACZgYGCzkDBuaGgw0MDDwgEQkitEgYGzAwomgxIKglcQNQC2MDVASvFv4ZuQ8fF9RIpG9nP9h4cEZFnYx5A/PB2zwMf/JxaZG4kW5sPOOYRO7OnsSGgxvOHOaROcCWbM3DYGDZgEOLgUQamzQPm0TuhgNALQ/bDvBIMPCYSQO14HQZUAv7b55/EukG5x8CtfyrA2rh/0ZICxszb5tEgsENoC0bG5hBtrDh1SJx5hmz9Mw+CcOdM4C2zDh2mEeCmc3Yco6BMU4t/O1pjJ8LvtnIm/MnH/7YU1NnL8He/PDGmwo5/BGDCpjBDiZBwygYBaNgFIwCDAAAoaxOVan1AMIAAAAASUVORK5CYII=","orcid":"","institution":"Balıkesir University","correspondingAuthor":true,"prefix":"","firstName":"İsa","middleName":"","lastName":"Cürebal","suffix":""}],"badges":[],"createdAt":"2026-03-25 04:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9218127/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9218127/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106094371,"identity":"1adb2d10-06f0-4322-83e3-97301b1b3ae8","added_by":"auto","created_at":"2026-04-03 11:42:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3155056,"visible":true,"origin":"","legend":"","description":"","filename":"LandslideSusceptibilityMappingaroundAlkumruDam.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9218127/v1_covered_2a7692d8-7105-4867-aaf4-d3f2067fbb51.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Landslide Susceptibility Mapping around Alkumru Dam (Siirt, Türkiye) Using Machine Learning and Ensemble Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Landslide Susceptibility Mapping, Machine Learning, Alkumru Dam Reservoir, Disaster Risk Management","lastPublishedDoi":"10.21203/rs.3.rs-9218127/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9218127/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed the landslide susceptibility of the Alkumru Dam Reservoir and its surrounding areas in Siirt Province, T\u0026uuml;rkiye, using six machine learning algorithms\u0026mdash;Support Vector Machines, Logistic Regression, Maximum Entropy, Naive Bayes, Random Forest, and Artificial Neural Networks\u0026mdash;alongside an Ensemble model. The analyses were conducted using MaxEnt and R software. A comprehensive landslide inventory and eleven conditioning factors were employed during the modeling process. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC) metric. All models demonstrated high predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.877\u0026ndash;0.959), with the Naive Bayes model achieving the highest performance (AUC\u0026thinsp;=\u0026thinsp;0.959). The spatial distribution of susceptibility maps was highly consistent across all algorithms. High and very-high-susceptibility zones were consistently identified on the southern, western, and southwestern slopes of the Alkumru Dam Reservoir and the right slope of the Botan River Valley\u0026mdash;areas characterized by weak lithological units with low mechanical strength. Quantitative validation confirmed that 52.2%-76.8% of mapped landslides fall within high- and very-high-risk zones. 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