Application of machine learning techniques for wetland type mapping in the Numto Nature Park (Western Siberia)

Application of machine learning techniques for wetland type mapping in the Numto Nature Park (Western Siberia) | 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 Application of machine learning techniques for wetland type mapping in the Numto Nature Park (Western Siberia) Mikhail Moskovchenko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4849462/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted 9 You are reading this latest preprint version Abstract The risk of melting West Siberian frozen mires increases because of current climate warming, which could affect the global carbon balance. Predicting the effects of warming requires assessing changes in the area of frozen mires. Geospatial models can be used for these purposes, as they allow for large-scale mapping of ecosystems over large areas. This study focuses on ecosystems of the Numto Nature Park (Western Siberia) where both frozen and unfrozen mires of various types are widespread. We used Landsat 8 satellite imagery and the ASTER GDEM digital elevation model as predictors and a vegetation map as a target variable. Fifteen different machine learning models were trained and compared with each other, including four classical machine learning models and eleven deep learning models (neural networks). Among classical machine learning models, the Gradient Boosting model achieved the best results (accuracy of 75.7%), and among neural networks, DeepLabV3 with ResNet50 backbone performed the best (accuracy of 75.8%). Therefore, both models yielded approximately the same accuracy in modeling different types of mires. However, the neural network was better at modeling palsa frozen mires and eutrophic and mesotrophic mires. In particular, the F1-scores of DeepLabV3 were 80.0% and 60.4%, while the Gradient Boosting model scored 78.5% and 56.5%, respectively. The mapping of the modeling results demonstrated that the use of neural networks makes it possible to preserve the original data structure and degree of data generalization. At the same time, the application of classical machine learning techniques results in "salt and pepper effect" and leads to the fragmentation of continuous areas but allows to identify smaller details. The models were successfully applied to map wetland types on land plots adjacent to the study area. Mires Western Siberia Spatial modeling Gradient Boosting Neural networks DeepLab Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 11 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers invited by journal 07 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 05 Aug, 2024 First submitted to journal 02 Aug, 2024 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. 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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-4849462","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344541032,"identity":"6ce7a88e-593c-4ca2-9839-63789486b10d","order_by":0,"name":"Mikhail Moskovchenko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYHACZhCRwMDABqQMEhj4wdwCUrRINoC4BkRrAdIGB0A0Hi267e2PDT622eXxS7clPrpRkCZnfH514ocHBgzy/GIHsGoxO3PGOHFmW3Kx5Jxjh41zDHKMzW683SwBdJjhzNkJ2LXcyGE+zHPmQOKGG+lt0jkGFYnbbpzdANKSYHAbl5b0x2At+6Fa6jfPOLv5B34tCcbJPBVAWyTSjgG15CQY8Pduw28L0C+GMyqSE2fcSEsG+iXNcMYN3m0WCQYSuP1yvP2xxAcDu8T+GWmGj3P+JMvz95/dfPNHhY08vzR2LViABFilBLHKQYD/ACmqR8EoGAWjYAQAAODtZGom4i/LAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tyumen","correspondingAuthor":true,"prefix":"","firstName":"Mikhail","middleName":"","lastName":"Moskovchenko","suffix":""}],"badges":[],"createdAt":"2024-08-02 15:33:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4849462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4849462/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12145-024-01654-3","type":"published","date":"2024-12-26T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72640770,"identity":"2b5c8859-7ec3-4918-ac96-5e79ff7c33e7","added_by":"auto","created_at":"2024-12-30 16:09:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570388,"visible":true,"origin":"","legend":"","description":"","filename":"ESIN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4849462/v1_covered_c1db348a-3e29-46c0-97bf-3ab8348a0ca3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of machine learning techniques for wetland type mapping in the Numto Nature Park (Western Siberia)","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":"earth-science-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esin","sideBox":"Learn more about [Earth Science Informatics](http://link.springer.com/journal/12145)","snPcode":"12145","submissionUrl":"https://submission.nature.com/new-submission/12145/3","title":"Earth Science Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mires, Western Siberia, Spatial modeling, Gradient Boosting, Neural networks, DeepLab","lastPublishedDoi":"10.21203/rs.3.rs-4849462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4849462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The risk of melting West Siberian frozen mires increases because of current climate warming, which could affect the global carbon balance. 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