Carbon Stock Modelling in Minas Gerais, Brazil: Effects of Dimensionality Reduction in Machine Learning Algorithms

Carbon Stock Modelling in Minas Gerais, Brazil: Effects of Dimensionality Reduction in Machine Learning Algorithms | 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 Carbon Stock Modelling in Minas Gerais, Brazil: Effects of Dimensionality Reduction in Machine Learning Algorithms Emerson Cristi de Barros, Gefferson Pereira da Paixão, José Augusto Amorim Silva do Sacramento, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8188444/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study aims to predict carbon stocks for the state of Minas Gerais, Brazil, using machine learning (Random Forest Regressor) and a set of 41 environmental variables. The objective was to explore the model's dimensionality reduction to map carbon. For variable selection, the Altmann permutation method and top-k selection test were applied. Only solar radiation (Srad), water deficit (Def), and the Palmer Drought Severity Index (Pdsi) had significant effects for the final model. The results demonstrated that the simplified model presented slightly lower precision metrics than the complete model. As a result, the final model exhibits greater interpretability and robustness. Furthermore, non-linear relationships were found between carbon stock, solar radiation, and water stress. It is concluded that climatic factors are key factors influencing carbon distribution in the state. Optimised models are tools that can be utilised in public policies for conservation and management. Carbon Stock Random Forest Environmental Variables Dimensionality Reduction Machine Learning Mapping Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 22 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 23 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. 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-8188444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564158331,"identity":"91ffd0f9-9830-4f72-8178-3b5a290981de","order_by":0,"name":"Emerson Cristi de 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