METHODS FOR PREDICTING SEISMIC ACTIVITY AND PREPARING FOR EARTHQUAKES USING MODERN TECHNOLOGIES

Abstract

This study explores the application of advanced AI techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), for earthquake prediction. By analyzing temporal and spatial seismic patterns, these models achieve a combined prediction accuracy of 75%, outperforming traditional methods. With a 30% reduction in latency and a 20% decrease in false positives, the AI models show promise for enhancing early warning systems. Despite data limitations in less-monitored regions, the findings suggest significant potential for global seismic preparedness through AI-driven solutions.