LUTanh Activation Function to Optimize BI-LSTM in Earthquake Forecasting

Abstract

Earthquakes, as unpredictable and potentially catastrophic events, have long captured the attention of geophysicists due to their profound impact on communities. The devastating consequences of these events underscore the critical need for early earthquake prediction systems capable of forecasting location, magnitude, and depth. With the rapid advancements in technology, particularly in the fields of data science, earthquake prediction methods have undergone significant evolution. This study introduces a proposed method that modified ReLU (Rectified Linear Unit) called LUTanh (Linear Unit Hyperbolic Tangent) which combine both benefits of ReLU and Tanh activation functions. This study applied and compared the proposed method performance in long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM) algorithms for predicting earthquake disaster. Furthermore, this proposed method was tested in architecture of multiple input multiple output (MIMO) principle in predicting the occurrence of earthquake disaster. The evaluation results decisively reveal that the LUTanh applied in Bi-LSTM model, particularly when optimized with the Adam optimizer, consistently outperforms the LSTM counterpart. Error assessments of LUTanh in Bi-LSTM consistently demonstrate lower average error scores compared to the origin ReLU activation function up to 4% of mean absolute error (MAE) and 3% of mean square error (MSE).