Year-Round Daily Wildfire Prediction and Key Factor Analysis Using Machine Learning: A Case Study of Gangwon State, South Korea

Year-Round Daily Wildfire Prediction and Key Factor Analysis Using Machine Learning: A Case Study of Gangwon State, 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 Year-Round Daily Wildfire Prediction and Key Factor Analysis Using Machine Learning: A Case Study of Gangwon State, South Korea Chanjung Lee, Eun Hyoung Choi, Youngju Han, Yohan Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6752516/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Under climate change and human’s dominant influence, wildfires have been increasing in frequency and scale, highlighting the demand of effective wildfire prediction and response. While prior research often maps high-risk areas, a few studies predict wildfire occurrences at specific dates and locations. This study aims to develop a year-round daily wildfire prediction model using machine learning, targeting Gangwon State in the Republic of Korea, and examine major influencing factors. We integrate meteorological elements (e.g., temperature, humidity, precipitation), forest-related variables (e.g., coniferous forest ratio, forest growing stock volume), and socioeconomic indicators (e.g., agricultural and cemetery land ratios) to identify salient predictors. We compare multiple algorithms, including Logistic Regression, XGBoost, and Random Forest, and use SHAP (SHapley Additive exPlanations) to enhance interpretability. The Extra Tree model achieves the highest AUC (0.839), and Random Forest demonstrates the best recall (0.828). SHAP results confirm that meteorological factors—especially relative humidity, precipitation, and temperature—are crucial, with forest- and socioeconomic variables also showing consistent effects. Applying a machine learning–based approach to daily wildfire prediction, integrating climate, environmental, and anthropogenic factors nationwide, and refining the temporal and spatial resolution of input data helps to advance wildfire prevention and response strategies in practice. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Environmental sciences wildfire daily wildfire prediction machine learning SMOTE SHAP Gangwon State Full Text Additional Declarations No competing interests reported. Supplementary Files submissionWildfirePaperLEEsupplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviews received at journal 12 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 04 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 26 May, 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. 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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-6752516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467026316,"identity":"ffec8e8b-bde6-4cf8-85d0-aa3fdc4d7974","order_by":0,"name":"Chanjung Lee","email":"","orcid":"","institution":"Department of Agriculture, Forestry and Bioresources, Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Chanjung","middleName":"","lastName":"Lee","suffix":""},{"id":467026317,"identity":"f5c2526d-44cf-423a-9cb8-0a33228291b1","order_by":1,"name":"Eun Hyoung Choi","email":"","orcid":"","institution":"Department of 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