Machine learning wildfire susceptibility mapping for Germany

Boris Thies ()
Additional contact information
Boris Thies: Philipps University Marburg

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 11, No 5, 12517-12530

Abstract: 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.

Keywords: Fire susceptibility; Machine learning; Germany; Remote sensing; Modeling (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07292-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:11:d:10.1007_s11069-025-07292-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07292-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().