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Machine learning wildfire susceptibility mapping for Germany

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  • Boris Thies

    (Philipps University Marburg)

Abstract

Wildfires present a significant threat to ecosystems and human life, particularly as global climate change amplifies the likelihood of extreme fire events. This study develops a machine learning-based wildfire susceptibility model for Germany, using data between 2003 and 2023. The primary goal is to identify the dominant wildfire predictors and create monthly susceptibility maps. A Random Forest (RF) algorithm was trained on remote sensing data and a comprehensive set of predictors, including meteorological, terrain, and land cover variables. The results indicate that surface air pressure, elevation, vegetation health, and proximity to urban areas are the most important factors in predicting fire susceptibility. The model achieved 89% accuracy, demonstrating the effectiveness of data-driven approaches in wildfire risk modeling. The monthly susceptibility map for July 2022 highlights northeastern Germany as particularly vulnerable to fire outbreaks. The results offer valuable insights for targeted wildfire prevention and resource allocation, emphasizing the importance of both temporal and spatial dimensions in managing wildfire risks.

Suggested Citation

  • Boris Thies, 2025. "Machine learning wildfire susceptibility mapping for Germany," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(11), pages 12517-12530, June.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:11:d:10.1007_s11069-025-07292-2
    DOI: 10.1007/s11069-025-07292-2
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    References listed on IDEAS

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    1. Ilaria Zambon & Artemi Cerdà & Pavel Cudlin & Pere Serra & Silvia Pili & Luca Salvati, 2019. "Road Network and the Spatial Distribution of Wildfires in the Valencian Community (1993–2015)," Agriculture, MDPI, vol. 9(5), pages 1-15, May.
    2. Meg A Krawchuk & Max A Moritz & Marc-André Parisien & Jeff Van Dorn & Katharine Hayhoe, 2009. "Global Pyrogeography: the Current and Future Distribution of Wildfire," PLOS ONE, Public Library of Science, vol. 4(4), pages 1-12, April.
    3. Guodong Wu & Jun Zhang & Heru Xue, 2023. "Long-Term Prediction of Hydrometeorological Time Series Using a PSO-Based Combined Model Composed of EEMD and LSTM," Sustainability, MDPI, vol. 15(17), pages 1-17, September.
    4. Hans-Guido Mücke & Jutta Maria Litvinovitch, 2020. "Heat Extremes, Public Health Impacts, and Adaptation Policy in Germany," IJERPH, MDPI, vol. 17(21), pages 1-14, October.
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