ADVANCED EARTHQUAKE PREDICTION: UNIFYING NETWORKS, ALGORITHMS, AND ATTENTION-DRIVEN LSTM MODELLING

Abstract

The study aims to develop an earthquake forecasting model based on Long Short-Term Memory (LSTM) networks with an embedded attention mechanism to improve the accuracy and reliability of forecasts that can be used in earthquake warning and mitigation applications. The objective is to explore and justify how this model can analytically improve the identification and interpretation of hidden patterns and anomalies in earthquake data to improve forecasting accuracy in seismically active regions such as Indonesia. The study used modeling techniques, analytical computations, and computer experimentation. The emphasis was placed on deep learning analysis to identify implicit indicators that could radically change the surveillance strategy and improve human safety. As a result, a model was built to illustrate the ability of LSTM networks with an embedded attention mechanism to improve earthquake forecasting by more accurately recognizing seismic patterns. This confirms the assumption that such networks can more effectively adapt to the identification of temporal dependencies in earthquake data. The model can detect and isolate seismic anomalies and precursors of major seismic events more effectively than standard forecasting approaches based on statistics and probability. The practical significance of the study lies in the opening of new opportunities for creating more accurate earthquake forecasting systems.