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

Mohamed S. Abdalzaher*, Hussein A. Elsayed, Mostafa M. Fouda*, Mahmoud M. Salim

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

45 Scopus citations

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.

Original languageEnglish
Article number495
JournalEnergies
Volume16
Issue number1
DOIs
StatePublished - Jan 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • disaster management
  • earthquake early warning system
  • Internet of Things
  • machine learning
  • smart city management

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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