Performance Analysis of Ensemble Deep Learning NARX System for Estimating the Earthquake Occurrences in the Subduction Zone of Java Island

Abstract

Earthquake is a natural hazard causing significant damage and loss of lives. In recent years, there has been a growing interest in the development of earthquake early warning system, using machine learning methods. One of the promising method is the use of Neural Network-Based Nonlinear AutoRegressive with eXogenous inputs (NN-based NARX) system, which has gained attention for the potential to improve the prediction accuracy and the robustness of earthquake early warning system. NN-based NARX system is composed of an effective recurrent neural network in modeling the time-series data. Therefore, this research aimed to investigate the performance of Ensemble Deep Learning NARX system, regarding earthquake occurrences estimation in the subduction zone, including Sunda Strait, Southern Java, and Bali Region. Ensemble Deep Learning NARX system was developed as the predictor to improve the performance characteristics of NN-based NARX system in determining earthquake occurrences in the subduction zone of Java Island. The proposed Ensemble model combined multiple NARX system, each trained on a different subset of earthquake data, using the diversity and complementarity of individual model. The results showed that Ensemble Deep Learning NARX system outperforms individual model and traditional method, yielding a significantly improved estimation performance. The mean square error (MSE) of the testing data set was 5.97x10-23, 8.97x10-24, 9.73x10-26 for Sunda Strait, Southern Java, and Bali Region, respectively. The research provided valuable insights for seismic hazard assessment, facilitating the development of proactive measures for earthquake mitigation and preparedness in the regions.

Keywords

• deep learning
• earthquake
• ensemble
• narx neural network
• subduction