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

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 an...

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Bibliographic Details
Main Authors: Chanjung Lee, Eun Hyoung Choi, Youngju Han, Yohan Lee
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Wildfire
Daily wildfire prediction
Machine learning
SMOTE
SHAP
Gangwon State
Online Access:https://doi.org/10.1038/s41598-025-15508-5
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https://doi.org/10.1038/s41598-025-15508-5

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