Earthquake Analysis Using Long-Short Term Memory - LSTM

The prediction and analysis of earthquakes have been a significant focus of scientific research for many years. Traditional methods have provided valuable insights, but their accuracy and dynamicity remain limited. The aim here is to address these challenges by employing deep learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, in conjunction with XGBoost as an ensemble learning technique. Earthquake analysis has been approached through various methodologies, with mixed results. Some methods have fallen short in delivering precise analysis, while others have grappled with the complexities of forecasting earthquakes. The introduction of deep learning in earthquake prediction has led to different outcomes compared to previous techniques. However, spatial distortion remains a persistent challenge. To tackle these challenges, this study utilizes the LSTM algorithm for global earthquake observation, combined with XGBoost. By employing influential and associated datasets, the model calculates a more accurate and straightforward time function. By combining the strengths of LSTM and XGBoost, this study aims to provide a more accurate and reliable earthquake analyzer model. The LSTM algorithm and XGBoost are addressing the challenges of spatial distortion and improving accuracy, dynamicity, and performance.