@article {10.3844/jcssp.2024.408.418,
article_type = {journal},
title = {Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey},
author = {Alkhatib, Ahmad and Jaber, Khalid},
volume = {20},
number = {4},
year = {2024},
month = {Feb},
pages = {408-418},
doi = {10.3844/jcssp.2024.408.418},
url = {https://thescipub.com/abstract/jcssp.2024.408.418},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}