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DOI: 10.14569/IJACSA.2020.0110564
PDF

Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning

Author 1: Wiame Benzekri
Author 2: Ali El Moussati
Author 3: Omar Moussaoui
Author 4: Mohammed Berrajaa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Due to the global warming, which mechanically increases the risk of starting fires. The number of forest fires is increasing and will increase more and more. To better support the fire soldiers on the ground, we present in this work a system for early detection of forest fires. This system is more precise compared to traditional surveillance approaches such as lookout towers and satellite surveillance. The proposed system is based on collecting environmental wireless sensor network data from the forest and predicting the occurrence of a forest fire using artificial intelligence, more particularly Deep Learning (DL) models. The combination of such a system based on the concept of the Internet of Things (IoT) is made up of a Low Power Wide Area Network (LPWAN), fixed or mobile sensors and a good suitable model of deep learning. That several models derived from deep learning were evaluated and compared enabled us to show the feasibility of an autonomous and real-time environmental monitoring platform for dynamic risk factors of forest fires.

Keywords: Forest fire detection; wireless sensor network; deep learning; internet of things; low power wide area network

Wiame Benzekri, Ali El Moussati, Omar Moussaoui and Mohammed Berrajaa, “Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110564

@article{Benzekri2020,
title = {Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110564},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110564},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Wiame Benzekri and Ali El Moussati and Omar Moussaoui and Mohammed Berrajaa}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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