Earthquake prediction using a hybrid deep learning model | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 11، دوره 11، شماره 2 - شماره پیاپی 21، اسفند 2025، صفحه 257-281 اصل مقاله (1.64 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2025.13249.1282 | ||
| نویسندگان | ||
| Seyed Hasan Mortazavi Zarch* 1؛ Javad Ezzati2؛ Fatemeh ZareMehrJerdi3؛ Mohsen SardariZarchi4 | ||
| 1Assistant Prof, Department of Computer Engineering, University of Meybod, Meybod, Iran | ||
| 2MSc. Student, Department of Computer Engineering, University of Meybod, Meybod, Iran | ||
| 3Assistant Prof, Department of Computer Engineering, University of Meybod, Meybod, | ||
| 4Associate Prof, Department of Computer Engineering, University of Meybod, Meybod, Iran | ||
| چکیده | ||
| In This study applies deep learning methods to predict earthquakes with magnitudes over 5.5 using a dataset of over 23,000 seismic events recorded from 1990 to 2024 in the Sarpol-e Zahab region. Several models were developed, including CNN, LSTM, Transformer, and a hybrid model combining CNN, LSTM, and Attention layers. The hybrid model demonstrated superior performance by capturing spatial patterns, temporal dependencies, and attention-based context, achieving 99.34% accuracy and a 0.0285 loss on the test set. Final evaluation yielded 99.51% accuracy, 96.59% precision, 93.92% recall, and a 95.24% F1-score, highlighting the model’s effectiveness in predicting potential earthquakes within a 30-day window., the results indicate that hybrid deep learning models offer valuable tools for developing intelligent early warning systems. this research contributes to improving seismic preparedness and risk reduction strategies in earthquake-prone regions. | ||
| کلیدواژهها | ||
| Earthquake Prediction؛ Deep Learning؛ . CNN؛ .LSTM | ||
| مراجع | ||
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