Unveiling Earthquake Dynamics: A Comprehensive Data Analytics and LSTM-Based Prediction Model for Enhanced Seismic Forecasting

This study provides a thorough examination of seismic occurrences, with a specific emphasis on the prediction of earthquakes. By extracting data from Wikipedia and the USGS’s API, we analyze earthquake records from 1900 to the present. Our investigation focuses on the temporal and spatial aspects, providing insights on the frequency, intensity, and consequential impacts of these earthquakes. Conventional methods, such as quantitative analysis and machine learning algorithms, frequently produce disappointing outcomes since earthquakes are dynamic and unpredictable. Therefore, we researched the relationships between the location and timing of earthquakes and employed long short-term memory (LSTM) networks to forecast future earthquakes. The forecasting capabilities of our LSTM model are superior due to its ability to exploit spatio-temporal linkages among earthquakes. In addition, we have conducted data analytics and visualization using Tableau, which has improved the clarity and ease of understanding of our findings [8]. In addition, we conducted a comparison between regular CPUs and GPUs for accelerated AI processing using LSTM. Our findings demonstrate that AI acceleration greatly improves the efficiency and speed of seismic data analysis, leading to more timely and accurate forecasts.