Hybrid Deep Learning Model for Earthquake Time Prediction

Utku A., AKCAYOL M. A.

Gazi University Journal of Science, cilt.37, sa.3, ss.1172-1188, 2024 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.35378/gujs.1364529
  • Dergi Adı: Gazi University Journal of Science
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Metadex, Civil Engineering Abstracts, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1172-1188
  • Anahtar Kelimeler: CNN, Deep learning, Earthquake, GRU, Machine learning
  • Gazi Üniversitesi Adresli: Evet

Özet

Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.