Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey

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Abstract

Forest fires are a persistent global problem, causing devastating consequences such as loss of human lives, harm to the environment, and substantial economic losses. To mitigate these impacts, the accurate prediction and early detection of forest fires is critical. In response to this challenge and living in the digital era of Artificial Intelligence (AI) and smart economies, there has been a growing interest in utilising AI mechanisms for forest fire management. This study provides an in-depth examination of the use of AI algorithms in the fight against forest fires. In particular, our paper starts with an overview of the forest fire problem, followed by a comprehensive review of various systems and approaches. This review includes a thorough analysis of the various works that have evaluated the factors that influence fire occurrence and severity, as well as those that focus on fire prediction and detection systems. The paper also explores the use of AI in adapting and restoring after the occurrence of forest fires. The paper concludes with an evaluation of the potential impact of AI on forest fire management and suggestions for future research directions, taking full advantage of novel technologies, such as 5G communications, Software Defined Networking (SDN), digital twins, federated learning and blockchain. Finally, the paper draws lessons and insights on the potential and limitations of AI in forest fire management, highlighting the need for further research and development in this field to maximise its impact and benefits.

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Giannakidou, S., Radoglou-Grammatikis, P., Lagkas, T., Argyriou, V., Goudos, S., Markakis, E. K., & Sarigiannidis, P. (2024, July 1). Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey. Internet of Things (Netherlands). Elsevier B.V. https://doi.org/10.1016/j.iot.2024.101171

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