Estimate the Burned Area Caused by Forest Fire with an Approach Based on Machine Learning

Document Type : Original Article

Authors

1 Master’s Student in artificial intelligence and robotics, Faculty of artificial intelligence and cognitive sciences, Imam Hossein Comprehensive University, Tehran, Iran

2 Assistant Professor, Faculty of artificial intelligence and cognitive sciences, Imam Hossein Comprehensive University, Tehran, Iran

Abstract

Forest fires are a serious threat to natural resources and critical infrastructure, causing extensive economic, environmental, and security damages annually. This research aims to strengthen passive defense and crisis management systems by presenting an innovative artificial intelligence-based approach for predicting burned area extent when facing forest fires. In this research, the XGBRegressor algorithm within machine learning framework was used to accurately predict the area damaged by fire using meteorological, geographical, and temporal data. The model achieves RMSE and MAE evaluation metrics of 61.602 and 12.273 respectively, enabling resource and equipment planning for fire suppression. The research findings demonstrate that through spatial-temporal fire analysis, critical points and high-risk periods can be identified. Additionally, the potential fire area can be estimated with minimal error, allowing targeted allocation of defensive resources. This approach, in line with intelligent territorial planning and proactive crisis management, enhances ability to protect critical infrastructures adjacent to forest areas. Moreover, this model can serve as a complement to an early warning system within the framework of passive defense and environmental resilience, playing a crucial role in reducing damages and preserving national resources. Although this study focuses on data from outside Iran, the presented research has the potential for localization to Iranian forests and can be used as a strategic tool in the country's passive defense.

Keywords

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References

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Passive Defense
Volume 17, Issue 1 - Serial Number 65
Serial number 65. Spring 2026
May 2026
Pages 19-35

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History

  • Receive Date: 13 March 2025
  • Revise Date: 02 June 2025
  • Accept Date: 20 September 2025
  • Publish Date: 22 May 2026

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