{"paper_id":"04e9404a-ea62-40b8-b134-aa08de3cbf8a","body_text":"Scenario-Based Land Use Change Prediction Using a Hybrid CA-Markov and XGBoost Model: A Case Study of Sejong City, South Korea | 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 Scenario-Based Land Use Change Prediction Using a Hybrid CA-Markov and XGBoost Model: A Case Study of Sejong City, South Korea Jiwon Lee, Jun-Hyuk Lee, Dong-In Won, Tae-Hyoung Tommy Gim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7022591/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study develops a hybrid modeling approach that integrates Cellular Automata (CA)-Markov with XGBoost-based logistic regression topredict future land use and land cover (LULC) changes under policy scenarios in Sejong City, South Korea. LULC data from 2005 to 2020is used to predict 2030 and 2035 under four development restriction scenarios: Strict Conservation (S1), Moderate Conservation (S2), Partial Development Allowed (S3), and Business As Usual (S4; BAU). The hybrid model outperforms logistic regression in simulating complex and nonlinear land transition processes(Area Under the Curve > 0.75, Kappa = 0.73). Stricter conservation policies result in increased forest area (55.8 km² under S1 by 2035), decreased agricultural land, and limited expansion of built-up and transportation infrastructure, according to scenario-based simulation. The BAU scenario illustrates the greatest reduction of agricultural land and the least forest expansion, highlighting the detrimental effects of unregulated development. The findings also emphasize barren land may adapt to ecological restoration and urban expansion based on the scenarios. Our findings support Sejong’s 2040 master plan, highlighting development control’s role in climate goals. This study shows methodological advantages of integrating machine learning into the CA-based spatial modelling improve decision support for sustainable urban growth management and carbon mitigation. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Land use and Land cover change CA-Markov model XGBoost logistic regression Scenario-Based modeling Urban Planning Carbon Neutrality Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7022591\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":487022914,\"identity\":\"d382d4ca-f268-486c-b9cf-2b1bb0770912\",\"order_by\":0,\"name\":\"Jiwon Lee\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Environmental Planning Institute, Seoul National University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiwon\",\"middleName\":\"\",\"lastName\":\"Lee\",\"suffix\":\"\"},{\"id\":487022916,\"identity\":\"0103d852-9d90-4265-95dd-9b49c46790a3\",\"order_by\":1,\"name\":\"Jun-Hyuk 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LULC data from 2005 to 2020is used to predict 2030 and 2035 under four development restriction scenarios: Strict Conservation (S1), Moderate Conservation (S2), Partial Development Allowed (S3), and Business As Usual (S4; BAU). The hybrid model outperforms logistic regression in simulating complex and nonlinear land transition processes(Area Under the Curve \\u0026gt; 0.75, Kappa = 0.73). Stricter conservation policies result in increased forest area (55.8 km² under S1 by 2035), decreased agricultural land, and limited expansion of built-up and transportation infrastructure, according to scenario-based simulation. The BAU scenario illustrates the greatest reduction of agricultural land and the least forest expansion, highlighting the detrimental effects of unregulated development. The findings also emphasize barren land may adapt to ecological restoration and urban expansion based on the scenarios. Our findings support Sejong’s 2040 master plan, highlighting \\u0026nbsp;development control’s role in climate goals. 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