Volume 16, Issue 1 (1-2026)
IJOCE 2026, 16(1): 93-106 |
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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-en.html
URL: http://ijoce.iust.ac.ir/article-1-666-en.html
1- School of Civil Engineering, Iran University of Science and Technology, PO Box 16846-13114, Iran
2- Housing and Urban Development Research Center, PO Code 463917151, Iran
2- Housing and Urban Development Research Center, PO Code 463917151, Iran
Abstract: (24 Views)
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.
Keywords: Earthquake data reconstruction, Deep neural network, ECBO algorithm, Seismic hazard assessment, Iran, Historical seismicity
Type of Study: Research |
Subject:
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Received: 2025/12/19 | Accepted: 2026/02/12 | Published: 2026/02/21
Received: 2025/12/19 | Accepted: 2026/02/12 | Published: 2026/02/21
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