Review Article Open Access

Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey

Ahmad Alkhatib1 and Khalid Jaber2
  • 1 Department of Cyber Security, Al-Zaytoonah University of Jordan, Airport St, Amman, Jordan
  • 2 Department of Computer Science, Al-Zaytoonah University of Jordan, Airport St, Amman, Jordan

Abstract

Forest fires are a major environmental challenge that poses a threat to both human life and ecological health. To effectively prevent and manage forest fires, it is crucial to have reliable detection, prediction and behavior analysis systems in place. This study provides a comprehensive survey of the different approaches and techniques used for forest fire detection, prediction and behavior analysis. It covers ground-based and aerial surveillance systems, remote sensing technologies, machine learning-based approaches and social media-based systems. The paper also discusses the challenges and limitations of current systems and provides insights into future directions for research and development in this field. Overall, this study highlights the importance of leveraging multiple data sources and analysis methods to improve our understanding of forest fire behavior and develop effective strategies for managing this environmental threat.

Journal of Computer Science
Volume 20 No. 4, 2024, 408-418

DOI: https://doi.org/10.3844/jcssp.2024.408.418

Submitted On: 24 October 2023 Published On: 7 February 2024

How to Cite: Alkhatib, A. & Jaber, K. (2024). Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey. Journal of Computer Science, 20(4), 408-418. https://doi.org/10.3844/jcssp.2024.408.418

  • 764 Views
  • 502 Downloads
  • 0 Citations

Download

Keywords

  • Forest Fire
  • Detection
  • Prediction
  • Behavior Analysis
  • Ground-Based Systems
  • Aerial Surveillance
  • Remote Sensing
  • Machine Learning
  • Social Media
  • Early Warning
  • Risk Assessment
  • Fire Management