AI-Enabled Earthquake Early Warning System Using Wireless Sensor Networks

Earthquake Early Warning Systems (EEWS) needless to mention is an essential tool in mitigating the effects of earthquakes by passing a warning to the affected region. The functionality of these systems presupposes the real-time identification and evaluation of seismic activity, and the modern development of automated detection protocols is considered to have boosted this factor significantly. This paper provides a critical analysis of the current approaches, tools, and platforms used in the detection and prevention of earthquakes. It also focuses on the advancements in the sensor systems like high sensitivity seismic sensors, fibre optic sensors, etc which were effectively incorporated and enhanced the capabilities of the seismic data acquisition. Further, the paper explores the machine learning technologies such as deep learning models and the hybrid model strategies used in the analysis of seismic data and prediction of earthquakes with higher efficiency. These algorithms help to detect those extremely subtle patterns and deviations that may characterize certain periods before the seismic activity, bringing the warnings earlier and more accurately. Data processing in real-time is also another and important emphasis of this class of reviews. Some of the developments highlighted in the paper include parallel computing, cloud computing, edge computing that make it easier to perform analysis and disseminate the warnings. Thus, employing these technologies, EEWS can give more prompt responses and accurate alerts, which contributes to increasing public safety and improving disaster readiness. The paper also consolidates a collection of case studies of different countries and zones containing Japan, Mexico, California, and Turkey. The present practical examples are intended to show the application of the automated detection procedures and to point out the working experience and problem fields connected with the use of the methods at different geographical and technological conditions.