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Assessing wildfire extents in Siberian forests using machine learning.

Ivan P Malashin1, Igor Masich2, Vladimir Nelyub2

  • 1Bauman Moscow State Technical University, 105005, Moscow, Russia. ivan.p.malashin@gmail.com.

Scientific Reports
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

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Machine learning models can predict wildfire size in Siberian forests using weather and forest data. XGBoost achieved 88.8% accuracy, identifying urban proximity and dry conditions as key factors for larger fires.

Area of Science:

  • Forestry
  • Ecology
  • Computer Science

Background:

  • Wildfires pose significant threats to global ecosystems and forest management.
  • Siberian forests, particularly in Krasnoyarsk Krai, are susceptible to large-scale fire events.
  • Accurate wildfire size estimation is crucial for effective management and ecological impact assessment.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) framework for estimating wildfire size in the Krasnoyarsk Krai region of Siberia.
  • To identify key environmental and meteorological factors influencing wildfire size.
  • To compare the performance of various ML models for wildfire size classification.

Main Methods:

  • Utilized a dataset integrating meteorological variables (temperature, humidity, wind, precipitation), forest composition, detection data, and historical fire records.
Keywords:
Climate factorsFire size estimationMachine learningSiberian forestsWildfire prediction

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  • Trained and compared multiple ML models: XGBoost, Random Forest, K-Nearest Neighbors, Logistic Regression, and Decision Tree.
  • Employed SHAP (SHapley Additive exPlanations) analysis to interpret model predictions and feature importance.

    • Main Results:

    • XGBoost demonstrated the highest classification accuracy at 88.8%, outperforming other tested ML models.
    • Feature importance analysis revealed that proximity to urban areas, wind patterns, and meteorological conditions affecting fuel moisture significantly influence fire size.
    • SHAP analysis indicated that localized weather conditions correlate with smaller fires, whereas prolonged dry periods are associated with larger fire events.

    Conclusions:

    • The developed ML framework shows strong potential for wildfire size classification in the studied Siberian region.
    • The findings highlight the critical role of meteorological factors and human proximity in determining wildfire extent.
    • The framework is currently exploratory and region-specific, necessitating local data calibration for broader applications.