Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models

Seda Şahin; Emine Çankaya

Makale detayları

Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models

Yazarlar:

Seda Şahin | Emine Çankaya

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.1253-1260

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353701

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 The Earths internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis. In this study, we studied with different deep learning models for earthquake time series prediction using Broadband Teleseismic Data from the USGS database. This dataset consists of 1000 seismic records in SAC format with long-period seismic waves from global earthquakes. The aim of this study was to test LSTM and RNN models with LSTM Transformer to predict the next time step based on previous seismic waves. In this study, model performances was evaluated with Mean Square Error MSE, Mean Absolute Error MAE and R² Score. In conclusion, the LSTM TransformerRNN model achieves the lowest error rates and presents its effectiveness in learning both short-term dependencies and long-term correlations in seismic data. At the same time, this study can also provides to the advancement of deep learning applications in seismology and the improvement of the prediction capabilities of earthquake monitoring systems.

 The Earths internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis. In this study, we studied with different deep learning models for earthquake time series prediction using Broadband Teleseismic Data from the USGS database. This dataset consists of 1000 seismic records in SAC format with long-period seismic waves from global earthquakes. The aim of this study was to test LSTM and RNN models with LSTM Transformer to predict the next time step based on previous seismic waves. In this study, model performances was evaluated with Mean Square Error MSE, Mean Absolute Error MAE and R² Score. In conclusion, the LSTM TransformerRNN model achieves the lowest error rates and presents its effectiveness in learning both short-term dependencies and long-term correlations in seismic data. At the same time, this study can also provides to the advancement of deep learning applications in seismology and the improvement of the prediction capabilities of earthquake monitoring systems.

 The Earths internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis. In this study, we studied with different deep learning models for earthquake time series prediction using Broadband Teleseismic Data from the USGS database. This dataset consists of 1000 seismic records in SAC format with long-period seismic waves from global earthquakes. The aim of this study was to test LSTM and RNN models with LSTM Transformer to predict the next time step based on previous seismic waves. In this study, model performances was evaluated with Mean Square Error MSE, Mean Absolute Error MAE and R² Score. In conclusion, the LSTM TransformerRNN model achieves the lowest error rates and presents its effectiveness in learning both short-term dependencies and long-term correlations in seismic data. At the same time, this study can also provides to the advancement of deep learning applications in seismology and the improvement of the prediction capabilities of earthquake monitoring systems.

 Dünyanın iç yapısını anlayarak sismik tehlikeleri azaltmak için deprem tahmini ve sismik dalga analizlerinin yapılması büyük önem taşımaktadır. Bu çalışmada, USGS veritabanından elde edilen Geniş Bant Teleseismik Verileri kullanarak deprem zaman serisi tahmini için farklı derin öğrenme modelleri ile çalışıldı. Bu veri seti, küresel depremlerden gelen uzun dönemli sismik dalgaları yakalayan 1000 adet SAC formatında sismik kayıttan oluşmaktadır. Bu çalışmada ki amacımız, önceki sismik dalgaları temel alarak bir sonraki zaman adımını tahmin etmek için LSTM Transformer ile geliştirilmiş LSTM ve RNN modellerinin denenmesini içermektedir. Bu çalışmada model performansları, Ortalama Karesel Hata MSE, Ortalama Mutlak Hata MAE ve R² Skoru kullanılarak değerlendirildi. Sonuç olarak LSTM TransformerRNN modelinin en düşük hata oranlarına ulaştığını ve sismik verilerdeki hem kısa vadeli bağımlılıkları hem de uzun vadeli korelasyonları öğrenmede etkinliğini gösterdiğini ortaya koymaktadır. Bu çalışma aynı zamanda, sismolojide derin öğrenme uygulamalarının ilerlemesine ve deprem izleme sistemlerinin tahmin yeteneklerinin geliştirilmesine katkıda bulunabilir.

Makale detayları

Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models

Yazarlar:

Seda Şahin | Emine Çankaya

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.1253-1260

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353701

pISSN:

2147-9526

Eartiquake Prediction,

Deep Learning

LSTM

Transformer

RNN

Seismic Time-SeriesDeprem Tahmini,

Derin Öğrenme,

LSTM,

Transformer,

RNN

Sismik Zaman Serisi

Sayı

Makale detayları

Yazarlar: Seda Şahin | Emine Çankaya

Yayıncı: Gazi University

Ana Başlıklar: Bilgi Sistemleri (Diğer)

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.1253-1260

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001353701

pISSN: 2147-9526

Sayı

Yazarlar:

Seda Şahin | Emine Çankaya

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.1253-1260

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001353701

pISSN:

2147-9526