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.
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This study has been funded and supported by Iranian Red Crescent Society.
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Seyed Mohammad Ayyoubzadeh conceptualized the research. Pirhossein Kolivand, Sharareh Rostam Niakan Kalhori and Peyman Saberian supervised the manuscript. Mozhgan Tanhapour, Fereshteh Karimi, Zohreh Javanmard, Soroush Heydari, Seyed Saeid Hoseini Talari, Seyed Mohsen Laal Mousavi, and Maryam Alidadi wrote the original draft. Mahnaz Ahmadi reviewed the manuscript.
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Kolivand, P., Saberian, P., Tanhapour, M. et al. A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence. Earth Sci Inform 17, 957–984 (2024). https://doi.org/10.1007/s12145-024-01253-2
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DOI: https://doi.org/10.1007/s12145-024-01253-2