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Forest Fire Detection and Notification Method Based on AI and IoT Approaches

Author

Listed:
  • Kuldoshbay Avazov

    (Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea)

  • An Eui Hyun

    (Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea)

  • Alabdulwahab Abrar Sami S

    (Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea)

  • Azizbek Khaitov

    (“DIGITAL FINANCE” Center for Incubation and Acceleration, Tashkent Institute of Finance, Tashkent 100000, Uzbekistan)

  • Akmalbek Bobomirzaevich Abdusalomov

    (Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea)

  • Young Im Cho

    (Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea)

Abstract

There is a high risk of bushfire in spring and autumn, when the air is dry. Do not bring any flammable substances, such as matches or cigarettes. Cooking or wood fires are permitted only in designated areas. These are some of the regulations that are enforced when hiking or going to a vegetated forest. However, humans tend to disobey or disregard guidelines and the law. Therefore, to preemptively stop people from accidentally starting a fire, we created a technique that will allow early fire detection and classification to ensure the utmost safety of the living things in the forest. Some relevant studies on forest fire detection have been conducted in the past few years. However, there are still insufficient studies on early fire detection and notification systems for monitoring fire disasters in real time using advanced approaches. Therefore, we came up with a solution using the convergence of the Internet of Things (IoT) and You Only Look Once Version 5 (YOLOv5). The experimental results show that IoT devices were able to validate some of the falsely detected fires or undetected fires that YOLOv5 reported. This report is recorded and sent to the fire department for further verification and validation. Finally, we compared the performance of our method with those of recently reported fire detection approaches employing widely used performance matrices to test the achieved fire classification results.

Suggested Citation

  • Kuldoshbay Avazov & An Eui Hyun & Alabdulwahab Abrar Sami S & Azizbek Khaitov & Akmalbek Bobomirzaevich Abdusalomov & Young Im Cho, 2023. "Forest Fire Detection and Notification Method Based on AI and IoT Approaches," Future Internet, MDPI, vol. 15(2), pages 1-13, January.
  • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:61-:d:1052910
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    References listed on IDEAS

    as
    1. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    2. Alessio Gagliardi & Sergio Saponara, 2020. "AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems," Energies, MDPI, vol. 13(8), pages 1-18, April.
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    Cited by:

    1. Pietro Battistoni & Andrea Antonio Cantone & Gerardo Martino & Valerio Passamano & Marco Romano & Monica Sebillo & Giuliana Vitiello, 2023. "A Cyber-Physical System for Wildfire Detection and Firefighting," Future Internet, MDPI, vol. 15(7), pages 1-28, July.
    2. Furkat Safarov & Mainak Basak & Rashid Nasimov & Akmalbek Abdusalomov & Young Im Cho, 2023. "Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection," Future Internet, MDPI, vol. 15(9), pages 1-19, September.

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