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Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment

Author

Listed:
  • Imran

    (Department of Computer Engineering, Jeju National University, Jeju 63243, Korea)

  • Naeem Iqbal

    (Department of Computer Engineering, Jeju National University, Jeju 63243, Korea)

  • Shabir Ahmad

    (Software Engineering Department University of Engineering & Technology Mardan, Mardan 23200, Pakistan
    Department of IT Convergence Engineering, Gachon University, Seongnam-Si 461-701, Korea)

  • Do Hyeun Kim

    (Department of Computer Engineering, Jeju National University, Jeju 63243, Korea)

Abstract

Mountains are popular tourist destinations due to their climate, fresh atmosphere, breathtaking sceneries, and varied topography. However, they are at times exposed to accidents, such as fire caused due to natural hazards and human activities. Such unforeseen fire accidents have a social, economic, and environmental impact on mountain towns worldwide. Protecting mountains from such fire accidents is also very challenging in terms of the high cost of fire containment resources, tracking fire spread, and evacuating the people at risk. This paper aims to fill this gap and proposes a three-fold methodology for fire safety in the mountains. The first part of the methodology is an optimization model for effective fire containment resource utilization. The second part of the methodology is a novel ensemble model based on machine learning, the heuristic approach, and principal component regression for predictive analytics of fire spread data. The final part of the methodology consists of an Internet of Things-based task orchestration approach to notify fire safety information to safety authorities. The proposed three-fold fire safety approach provides in-time information to safety authorities for making on-time decisions to minimize the damage caused by mountain fire with minimum containment cost. The performance of optimization models is evaluated in terms of execution time and cost. The particle swarm optimization-based model performs better in terms of cost, whereas the bat algorithm performs better in terms of execution time. The prediction models’ performance is evaluated in terms of root mean square error, mean absolute error, and mean absolute percentage error. The proposed ensemble-based prediction model accuracy for fire spread and burned area prediction is higher than that of the state-of-the-art algorithms. It is evident from the results that the proposed fire safety mechanism is a step towards efficient mountain fire safety management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2461-:d:505404
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    References listed on IDEAS

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    1. Zheng, Zhong & Huang, Wei & Li, Songnian & Zeng, Yongnian, 2017. "Forest fire spread simulating model using cellular automaton with extreme learning machine," Ecological Modelling, Elsevier, vol. 348(C), pages 33-43.
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    3. Fazli Wahid & Muhammad Fayaz & Ayman Aljarbouh & Masood Mir & Muhammad Aamir & Imran, 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms," Energies, MDPI, vol. 13(17), pages 1-26, August.
    4. Jung Wook Park & Ohk Kun Lim & Woo Jun You, 2020. "Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation," Energies, MDPI, vol. 13(14), pages 1-15, July.
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    Cited by:

    1. 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.
    2. Imran & Faisal Jamil & Dohyeun Kim, 2021. "An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
    3. Amit Sundas & Sumit Badotra & Salil Bharany & Ahmad Almogren & Elsayed M. Tag-ElDin & Ateeq Ur Rehman, 2022. "HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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