Comparative Analysis of LSTM and Bi-LSTM Models for Earthquake Occurrence Prediction in Tokai-Japan Region

Azhari Haris Al Hamdi; Hapsoro Agung Nugroho; Benyamin Kusumoputro

doi:10.62146/ijecbe.v2i4.87

Authors

Azhari Haris Al Hamdi Universitas Indonesia
Hapsoro Agung Nugroho Universitas Indonesia
Benyamin Kusumoputro Universitas Indonesia

DOI:

https://doi.org/10.62146/ijecbe.v2i4.87

Keywords:

LSTM, USGS, Tokai, earthquake, deep learning

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in predicting earthquake occurrences in the Tokai region, using data from the United States Geological Survey (USGS) dataset. Given the importance of accurate earthquake prediction, particularly in high-risk regions, this research focuses on assessing the effectiveness of each model in identifying occurrence and non-occurrence events. Both models were tuned to optimize sensitivity and specificity through adjustments in sequence length, learning rate, and additional hyperparameters, with results evaluated using metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Findings reveal that while both models achieved high sensitivity, the LSTM model demonstrated superior specificity and AUC, indicating a more balanced performance in distinguishing between earthquake occurrences and non-occurrences. The results show that LSTM outperforms Bi-LSTM in terms its classification metrics. LSTM achieved an accuracy of 76%, compared to 55% for Bi-LSTM. For the AUC metric, LSTM scored 66%, while Bi-LSTM scored 67%.

Author Biographies

Azhari Haris Al Hamdi, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Hapsoro Agung Nugroho, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Benyamin Kusumoputro, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

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