Modeling and Predicting Land Use/Land Cover Change Using Deep Learning in southern Malawi

Modeling and Predicting Land Use/Land Cover Change Using Deep Learning in southern Malawi | 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 Modeling and Predicting Land Use/Land Cover Change Using Deep Learning in southern Malawi Harineck Mayamiko Tholo, Chikondi Chisenga, Emmanuel Chinkaka, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6991880/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Monitoring land use and land cover (LULC) change is crucial for ecological sustainability and urban planning. This study employs a Multi-Layer Perceptron (MLP) neural network and Random Forest classifier to model LULC dynamics in Malawi’s Michiru Mountain Forest Reserve from 2004 to 2024. During this period, vegetation cover declined from 85.77 km² to 31.55 km² (63% loss), while bare land increased by 123% and built-up areas nearly doubled from 4.58 km² to 8.77 km². Spatial analysis shows 70% of urban expansion occurred within 500 meters of roads, emphasizing the role of infrastructure. The optimized MLP model achieved 99.87% accuracy, a 0.9974 skill measure, and a Kappa coefficient of 0.85, with minimal overfitting (training RMS: 0.0368, testing RMS: 0.0396). Forecasts for 2034 project built-up areas to reach 23.20 km² and further vegetation decline, highlighting ecosystem degradation and the need for integrated governance. Land Use Land Cover Change (LULC) Machine Learning (MLP Random Forest) Deforestation Urban Expansion Michiru Mountain Forest Reserve Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor invited by journal 26 Jul, 2025 Editor assigned by journal 30 Jun, 2025 Submission checks completed at journal 30 Jun, 2025 First submitted to journal 27 Jun, 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. 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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-6991880","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492623943,"identity":"dbe71c11-9296-45db-b248-486fb9eb71ed","order_by":0,"name":"Harineck Mayamiko 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This study employs a Multi-Layer Perceptron (MLP) neural network and Random Forest classifier to model LULC dynamics in Malawi\u0026rsquo;s Michiru Mountain Forest Reserve from 2004 to 2024. During this period, vegetation cover declined from 85.77 km\u0026sup2; to 31.55 km\u0026sup2; (63% loss), while bare land increased by 123% and built-up areas nearly doubled from 4.58 km\u0026sup2; to 8.77 km\u0026sup2;. Spatial analysis shows 70% of urban expansion occurred within 500 meters of roads, emphasizing the role of infrastructure. The optimized MLP model achieved 99.87% accuracy, a 0.9974 skill measure, and a Kappa coefficient of 0.85, with minimal overfitting (training RMS: 0.0368, testing RMS: 0.0396). 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