دوره 16، شماره 1 - ( 11-1404 )
جلد 16 شماره 1 صفحات 106-93 |
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Kaveh A, Beitollahi A, Khavaninzadeh N. RECONSTRUCTING HISTORICAL EARTHQUAKE DATA FOR IRAN USING A DEEP NEURAL NETWORK OPTIMIZED BY ECBO ALGORITHM. IJOCE 2026; 16 (1) :93-106
URL: http://ijoce.iust.ac.ir/article-1-666-fa.html
URL: http://ijoce.iust.ac.ir/article-1-666-fa.html
RECONSTRUCTING HISTORICAL EARTHQUAKE DATA FOR IRAN USING A DEEP NEURAL NETWORK OPTIMIZED BY ECBO ALGORITHM. عنوان نشریه. 1404; 16 (1) :93-106
چکیده: (25 مشاهده)
This study develops a synthetic earthquake catalog for Iran (1900–1963) using a deep neural network (DNN) optimized by the Enhanced Colliding Bodies Optimization (ECBO) algorithm. The model, trained on post-1964 instrumental data from the Iranian Seismological Center, incorporates spatial, temporal, and tectonic features to estimate earthquake magnitudes. Statistical indices (MAE = 0.0064; RMSE = 0.3748) and bootstrap uncertainty analysis (±0.18 M) confirm the model’s reliability. The generated catalog provides a data-driven basis for improving seismic hazard assessment and historical seismicity reconstruction across the Iranian plateau.
نوع مطالعه: پژوهشي |
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دریافت: 1404/9/28 | پذیرش: 1404/11/23 | انتشار: 1404/12/2
دریافت: 1404/9/28 | پذیرش: 1404/11/23 | انتشار: 1404/12/2
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