پیش‌بینی زلزله با استفاده از مدل ترکیبی یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه کامپیوتر, ,دانشکده فنی و مهندسی دانشگاه میبد میبد ایران

2 دانشجوی کارشناسی ارشد، دانشکده مهندسی کامپیوتر، دانشگاه میبد، میبد، ایران

3 استادیار، دانشکده مهندسی کامپیوتر، دانشگاه میبد، میبد، ایران

4 دانشیار، دانشکده مهندسی کامپیوتر، دانشگاه میبد، میبد، ایران؛

چکیده

در این پژوهش، از یادگیری عمیق برای پیش‌بینی زلزله‌هایی با بزرگای بیش از ۵.۵ استفاده شده است. داده‌های مورد استفاده شامل بیش از ۲۳ هزار رویداد لرزه‌ای ثبت‌شده در بازه زمانی ۱۹۹۰ تا ۲۰۲۴ در منطقه سرپل‌ذهاب و نواحی اطراف آن است. و چندین مدل یادگیری عمیق طراحی و پیاده‌سازی شد که شامل شبکه‌های عصبی پیچشی، حافظه بلندمدت، مدل‌های مبدل و همچنین یک معماری ترکیبی از این سه مدل بود. مدل ترکیبی با بهره‌گیری همزمان از قابلیت‌های استخراج ویژگی‌های مکانی، شناسایی وابستگی‌های زمانی و تمرکز تطبیقی، عملکرد بهتری نسبت به سایر مدل‌ها نشان داد و به دقت ۹۹.۳۴ درصد و خطای ۰.۰۲۸۵ در داده‌های آزمون دست یافت. در ارزیابی نهایی، این مدل دقت ۹۹.۵۱ درصد، شاخص صحت ۹۶.۵۹ درصد، بازیابی ۹۳.۹۲ درصد و امتیاز نهایی ۹۵.۲۴ درصد را ثبت کرد که کارایی بالای آن را در پیش‌بینی زلزله‌های احتمالی در بازه ۳۰ روزه تأیید می‌کند. نتایج این پژوهش نشان می‌دهد که استفاده راهبردی از مدل‌های ترکیبی یادگیری عمیق می‌تواند نقش مهمی در توسعه سامانه‌های هوشمند هشدار زلزله و کاهش خسارات انسانی و مالی ایفا کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Earthquake prediction using a hybrid deep learning model

نویسندگان [English]

  • Seyed Hasan Mortazavi Zarch 1
  • Javad Ezzati 2
  • Fatemeh ZareMehrJerdi 3
  • Mohsen SardariZarchi 4
1 Assistant Prof, Department of Computer Engineering, University of Meybod, Meybod, Iran
2 MSc. Student, Department of Computer Engineering, University of Meybod, Meybod, Iran
3 Assistant Prof, Department of Computer Engineering, University of Meybod, Meybod,
4 Associate Prof, Department of Computer Engineering, University of Meybod, Meybod, Iran
چکیده [English]

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 [English]

  • Earthquake Prediction
  • Deep Learning
  • . CNN
  • .LSTM
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