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Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Author

Listed:
  • Mohamed S. Abdalzaher

    (Department of Seismology, National Research Institute of Astronomy and Geophysics, Cairo 11421, Egypt)

  • Hussein A. Elsayed

    (Department of Electronics and Communications Engineering, Ain Shams University (ASU), Cairo 11566, Egypt)

  • Mostafa M. Fouda

    (Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA)

  • Mahmoud M. Salim

    (Department of Electronics and Communications Engineering, October 6 University (O6U), Giza 12585, Egypt
    School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

Abstract

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.

Suggested Citation

  • Mohamed S. Abdalzaher & Hussein A. Elsayed & Mostafa M. Fouda & Mahmoud M. Salim, 2023. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities," Energies, MDPI, vol. 16(1), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:495-:d:1022725
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    References listed on IDEAS

    as
    1. Tzu-Hsuan Lin & Jing-Ting Huang & Alan Putranto, 2022. "Integrated smart robot with earthquake early warning system for automated inspection and emergency response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 765-786, January.
    2. Erik L. Olson & Richard M. Allen, 2005. "The deterministic nature of earthquake rupture," Nature, Nature, vol. 438(7065), pages 212-215, November.
    3. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
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    Cited by:

    1. Tao Li & Jianqiang Luo & Kaitong Liang & Chaonan Yi & Lei Ma, 2023. "Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    2. Mohamed S. Abdalzaher & Mostafa M. Fouda & Ahmed Emran & Zubair Md Fadlullah & Mohamed I. Ibrahem, 2023. "A Survey on Key Management and Authentication Approaches in Smart Metering Systems," Energies, MDPI, vol. 16(5), pages 1-27, March.
    3. Mohamed S. Abdalzaher & Moez Krichen & Derya Yiltas-Kaplan & Imed Ben Dhaou & Wilfried Yves Hamilton Adoni, 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey," Sustainability, MDPI, vol. 15(15), pages 1-38, July.

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