Predicting the Unpredictable: Advancements in Earthquake Forecasting Using Artificial Intelligence and LSTM Networks

Abstract

Earthquakes, representing some of the most devastating natural phenomena, pose a persistent challenge in prediction due to their unpredictable nature. Despite significant insights offered by traditional methods, their lack of precision often leaves communities at risk. This research explores a cutting-edge approach in earthquake prediction using artificial intelligence (AI), with a particular emphasis on the attention encoder-decoder long short-term memory (LSTM) model. Focused on modeling seismic events of varied scales that occurred from 2007 to 2010 in the North Anatolian Fault Zone (NAFZ), Turkey, this study compares the efficacy of various AI models including multi-layer perceptron (MLP) and LSTM. The research reveals that the attention encoder-decoder LSTM model surpasses its counterparts in performance. It demonstrates a remarkable capability in forecasting earthquakes by effectively deciphering complex patterns within the data, underscoring its viability as a powerful tool in seismic prediction. The attention encoder-decoder LSTM model, utilizing AI's latest advancements, offers a nuanced approach by selectively concentrating on relevant data segments, a method particularly beneficial in analyzing complex seismic patterns. This study endeavors to advance the field of earthquake prediction, proposing a model that combines sophisticated AI techniques with in-depth seismic data analysis for more accurate forecasting.