Sensor-Based Systems for Early Detection and Prediction of Forest Fires
Abstract
Forest fires pose a threat to ecosystems, wildlife, and human life. Early detection and prediction of forest fires is crucial to fire prevention and mitigation strategies. In recent years, sensor-based systems have become powerful tools for monitoring and early detection of forest fires. This article examines the various techs used in forest fire detection and prediction, how they work, and their potential to increase firefighting effectiveness. It discusses the integration of sensor networks and data acquisition/transmission systems to establish effective monitoring networks. Moreover, the paper investigates the application of machine learning and artificial intelligence approaches for early warning and prediction models. It also addresses the challenges associated with sensor-based systems, including sensor reliability, power supply, and data processing. Finally, the paper identifies future research directions to enhance the capabilities of sensor-based systems for forest fire management.
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2. https://www.britannica.com/science/forest-fire
3. A. Ager, B. Bahro, N. L. Staus, M. A. Day, K. C. Short, C. R. Evers, and H. K. Preisler, "Wildfire exposure analysis framework: estimating wildfire exposure and effects on vegetation condition across large landscapes," Environmental Monitoring and Assessment, vol. 189, no. 8, p. 377, Aug. 2017.
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6. https://www.hindawi.com/journals/cin/2022/3170244/
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28. J. Zhang et al., "Early detection of forest fires based on an IoT wireless sensor network," International Journal of Distributed Sensor Networks, vol. 17, no. 1, p. 1550147721994577, Feb. 2021.
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Published
2023-12-29
How to Cite
SINGH, Gourav.
Sensor-Based Systems for Early Detection and Prediction of Forest Fires.
Journal of Advanced Research in Applied Artificial Intelligence and Neural Network, [S.l.], v. 7, n. 2, p. 29-35, dec. 2023.
Available at: <http://thejournalshouse.com/index.php/neural-network-intelligence-adr/article/view/947>. Date accessed: 03 may 2024.
Section
Review Article
