Deep Neural Networks in Earthquake Prediction Models and Automatic Formulation of Seismic Strategies

Earthquakes are considered one of the most destructive disasters in nature. The study collected and organized historical earthquake data, utilizing the powerful feature extraction and pattern recognition capabilities of deep learning to train deep neural network models through these historical data to identify key geological changes that may cause earthquakes. In addition, combined with earthquake risk assessment, this study also developed an automatic system for formulating earthquake resistance strategies, which can automatically recommend corresponding disaster prevention and reduction measures based on model prediction results, such as optimizing evacuation routes and emergency resource allocation. During the simulated real-time data reception process, the system was able to process seismic data and generate prediction results in less than 1 second (with an average response time of about 0.95 seconds), demonstrating its efficient response capability. The automatic formulation of earthquake resistance strategies in this article not only enhances the efficiency and effectiveness of earthquake emergency management, but also provides new research directions and methods for the field of disaster science.