Real-Time Earthquake Detection and Intensity Forecasting System

Earthquakes pose a severe threat to life and infrastructure, necessitating efficient real-time detection and accurate intensity forecasting. This research presents a Real-Time Earthquake Detection and Intensity Forecasting System that utilizes ADXL335 accelerometers and Arduino Uno microcontrollers for seismic data acquisition. The system processes real-time acceleration data, applies noise filtering, and detects seismic events based on predefined thresholds. To enhance forecasting accuracy, a Long Short-Term Memory (LSTM) neural network is employed, leveraging historical seismic patterns for precise magnitude prediction. The integration of sensor-based data collection with deep learning improves system reliability, enabling timely alerts and early warnings. Experimental results demonstrate the system’s effectiveness in detecting seismic activity with high accuracy and minimal false positives. This research contributes to real-time earthquake monitoring, offering a scalable and cost-effective solution for early warning applications