Low-Cost Real-Time Earthquake Detection and Early Warning System Using IoT, Embedded Machine Learning
Abstract
Abstract Earthquakes are among the most destructive natural events, causing widespread loss of life, damage to infrastructure, and lasting economic consequences. Conventional seismic forecasting methods rely heavily on historical records and manual statistical analysis, making it difficult to capture complex, evolving patterns in seismic activity or issue timely warnings. This paper presents a machine learning–based seismic forecasting system trained on historical earthquake data—incorporating parameters such as magnitude, depth, latitude, longitude, and timestamp—using Random Forest, Support Vector Machine (SVM), and Random Forest, Gradient boosting ,Logistic regression algorithms to uncover hidden patterns and improve prediction of future seismic events. The best-performing model is deployed on a ESP32for real-time edge inference, enabling operation in areas with limited internet connectivity. A GPS module provides accurate geolocation for predicted events, while a GSM module delivers instant SMS alerts containing risk level and coordinates to both authorities and end users. The resulting system is affordable, energy-efficient, and scalable, demonstrating how machine learning and IoT hardware can be combined to build practical, real-time disaster early warning systems.
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