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Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. Most recent devastating earthquakes have magnitudes greater than > M8. Satellite data, global positioning system, interferometry synthetic aperture radar (InSAR), and seismometers such as microelectromechanical system, seismometers, ocean bottom seismometers, and distributed acoustic sensing systems have all been used to predict earthquakes with a high degree of success. Despite advances in seismic wave recording, storage, and analysis, earthquake time, location, and magnitude prediction remain difficult. On the other hand, new developments in artificial intelligence (AI) and the Internet of Things (IoT) have shown promising potential to deliver more insights and predictions. Thus, this article reviewed the use of AI-driven Models and IoT-based technologies for the prediction of earthquakes, the limitations of current approaches, and open research issues. The review discusses earthquake prediction setbacks due to insufficient data, inconsistencies, diversity of earthquake precursor signals, and the earth's geophysical composition. Finally, this study examines potential approaches or solutions that scientists can employ to address the challenges they face in earthquake prediction. The analysis is based on the successful application of AI and IoT in other fields.
Pwavodi et al. (Tue,) studied this question.
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