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Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models

Author

Listed:
  • Jukyeong Choi

    (Department of Forestry and Environmental Systems, Kangwon National University, Chuncheon 24341, Republic of Korea)

  • Youngjo Yun

    (Department of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341, Republic of Korea)

  • Heemun Chae

    (Division of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea)

Abstract

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data.

Suggested Citation

  • Jukyeong Choi & Youngjo Yun & Heemun Chae, 2025. "Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models," Land, MDPI, vol. 14(6), pages 1-16, May.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:6:p:1155-:d:1665398
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    References listed on IDEAS

    as
    1. Saad Mazhar Khan & Imran Shafi & Wasi Haider Butt & Isabel de la Torre Diez & Miguel Angel López Flores & Juan Castanedo Galán & Imran Ashraf, 2023. "A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions," Land, MDPI, vol. 12(8), pages 1-37, July.
    2. Zhiming Xia & Kaitao Liao & Liping Guo & Bin Wang & Hongsheng Huang & Xiulong Chen & Xiangmin Fang & Kuiling Zu & Zhijun Luo & Faxing Shen & Fusheng Chen, 2025. "Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China," Land, MDPI, vol. 14(1), pages 1-20, January.
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