Earthquake Prediction for the Düzce Province in the Marmara Region Using Artificial Intelligence

Pura T., Güneş P., Güneş A., Hameed A. A.

Applied Sciences (Switzerland), cilt.13, sa.15, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 15
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/app13158642
  • Dergi Adı: Applied Sciences (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: artificial neural network, earthquake, prediction, recurrent neural network
  • İstanbul Gelişim Üniversitesi Adresli: Hayır

Özet

By definition, an earthquake is a naturally occurring event. This natural event may be a disaster that causes significant damage, loss of life, and other economic effects. The possibility of predicting a natural event such as an earthquake will minimize the negative effects mentioned above. In this study, data collection, processing, and data evaluation regarding earthquakes were carried out. Earthquake forecasting was performed using the RNN (recurrent neural network) method. This study was carried out using seismic data with a magnitude of 3.0 and above of the Düzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of earthquakes were calculated. The detection of earthquakes within a specific time interval in the Marmara region of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the generation of predictions for the future highlight the importance of this study. Our results demonstrated that the prediction performance could be significantly improved by incorporating the b and d coefficients of earthquakes, as well as the data regarding the distance between the Moon and the Earth, along with the use of recurrent neural networks (RNNs).