IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i1p495-d1022725.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/495/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/495/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    3. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
    4. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    5. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    6. Rui Zhang & Hao Zhang & Jianqiao Ye & Jiaqing Wang & Qing Liu & Shenghu Li, 2023. "Eigen-Sensitivity-Based Sliding Mode Control for LFO Damping in DFIG-Integrated Power Systems," Energies, MDPI, vol. 16(10), pages 1-18, May.
    7. 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.
    8. Luigi Rubino & Guido Rubino & Raffaele Esempio, 2023. "Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station," Energies, MDPI, vol. 16(3), pages 1-17, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:495-:d:1022725. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.