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A Systematic Review of Earthquake Early Warning (Eew) Systems Based on Artificial Intelligence Publisher



Kolivand P1 ; Saberian P2 ; Tanhapour M3 ; Karimi F4 ; Kalhori SRN3 ; Javanmard Z3 ; Heydari S3 ; Talari SSH3 ; Mousavi SML3 ; Alidadi M5 ; Ahmadi M6 ; Ayyoubzadeh SM3, 4, 7
Authors
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Authors Affiliations
  1. 1. Department of Health Economics, Faculty of Medicine, Shahed University, Tehran, Iran
  2. 2. Department of Anesthesiology, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Research Center for Emergency and Disaster Resilience, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran
  5. 5. Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  6. 6. Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  7. 7. Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran

Source: Earth Science Informatics Published:2024


Abstract

Early Earthquake Warning (EEW) systems alarm about ongoing earthquakes to reduce their devastating human and financial damages. In complicated tasks like earthquake forecasting, Artificial Intelligence (AI) solutions show promising results. The goal of this review is to investigate the AI-based EEW systems. Web of Science, Scopus, Embase, and PubMed databases were systematically searched from its beginning until April 18, 2023. Studies that used AI algorithms to develop EEWs and forecast earthquake magnitude were qualified. The quality assessment was conducted using the Mixed Methods Assessment Tool version 2018. Detailed analysis was performed on 26 of 2604 retrieved articles. Researchers predict earthquakes most often using neural network family models (21 studies). Among eight categorized groups of parameters for earthquake forecasting, it was often predicted utilizing seismic wave characteristics (65.38%) and seismic activity data (61.54%). AI models most often predicted earthquake magnitude (32.69%) and depth (15.38%). Logistic Model Tree and Bayesian Network had the highest sensitivity, accuracy, and F-measure efficiency (99.9%). Findings showed that AI algorithms can forecast earthquakes. However, additional study is needed to determine the efficacy of more data-driven AI algorithms in mining seismic data using more input variables. This review is helpful for seismologists and researchers developing EEW systems using AI. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.