A Review on Seismic Intelligence: Bridging AI and IoT for Predictive Earthquake Mitigation
Keywords:
AIIoT, Artificial Intelligence, Decision-making, Earthquake, Internet of Things (IoT)Abstract
The catastrophic impact of seismic events necessitates a paradigm shift from reactive emergency responses to proactive, real-time predictive modeling. This paper explores the integration of Artificial Intelligence (AI) with the Internet of Things (IoT) to revolutionize earthquake detection and decision-making. By deploying dense, low-cost sensor networks capable of capturing high-frequency seismic vibrations, it can create a distributed web of "seismic ears" that transmit data to edge-computing nodes. It proposes a hybrid architecture where Machine Learning (ML) algorithms, specifically Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), process streaming IoT data to distinguish between ambient anthropogenic noise and genuine pre-seismic tremors. This framework facilitates automated, millisecond-latency decision-making, including the triggering of smart-grid shutdowns, automated transport halts, and instant wide-area early warnings. By reducing the reliance on sparse, high-cost seismic stations, this research demonstrates that a decentralized AI-driven IoT approach can significantly enhance the resolution of geological monitoring and drastically reduce the window of uncertainty in disaster mitigation.
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