Earthquake prediction optimization using deep learning hybrid RNN-LSTM model for seismicity analysis

Abstract

Earthquakes are among the most destructive natural disasters, posing severe risks to human life and infrastructure. Accurate and reliable earthquake forecasting systems are crucial for effective disaster management and mitigation. Recent advancements in machine learning and deep learning present promising pathways for enhancing earthquake prediction accuracy. This study provides an in-depth investigation of machine learning methods for earthquake forecasting, emphasizing their critical role in disaster prevention. Four deep learning models are evaluated: Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), AdaBoost, and a hybrid RNN-LSTM model. The RNN-LSTM hybrid model demonstrates exceptional performance by leveraging the strength of LSTM in capturing long-term dependencies and RNN in detecting short-term patterns, allowing for a comprehensive analysis of seismic activity. Among these models, the RNN-LSTM hybrid stands out, achieving an impressive accuracy rate of 98 %, significantly surpassing the other models. These results highlight the potential of machine learning technologies to improve earthquake prediction precision. The proposed approach enhances current forecasting methods, offering more accurate and reliable earthquake predictions. This research makes a substantial contribution to disaster preparedness and mitigation.