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<p>This study presents a hybrid predictive model that integrates Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) to forecast earthquake timing and magnitude in Saman, Iran, with a strong focus on vibration signal analysis and dynamic measurement. The offered model implements 12 vibration-based input features, including peak ground acceleration (PGA), shear wave velocity, and spectral intensity, all of which are derived from seismotectonic and accelerometer data. PSO optimizes ANN weight initialization. This approach enhances the model’s ability to accurately represent seismic wave dynamics, making it well-suited for applications in vibration engineering. The dataset consisted of historical seismic records and was divided into 80% for training and 20% for testing. The ANN-PSO model outperformed conventional ANN and SVM, achieving denormalized RMSE of 0.152 (magnitude) and 0.189 (timing window likelihood error), MAE of 0.118 and 0.147, R² of 0.958 and 0.941, and Pearson r of 0.979 and 0.970 across 20 runs (MSE 0.023 on normalized scale, equivalent after denormalization). Thus, it was identified as a robust tool in the fields of vibration-based seismic forecasting, structural health monitoring, and mechanical reliability analysis in tectonically active regions.</p>

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Designing a model for earthquake timing and magnitude prediction based on neural networks and particle swarm optimization (PSO) algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Ramin Vafaei Poursorkhabi,
  • Ata Rezaei Fard,
  • Ali Rezaei Fard

摘要

This study presents a hybrid predictive model that integrates Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) to forecast earthquake timing and magnitude in Saman, Iran, with a strong focus on vibration signal analysis and dynamic measurement. The offered model implements 12 vibration-based input features, including peak ground acceleration (PGA), shear wave velocity, and spectral intensity, all of which are derived from seismotectonic and accelerometer data. PSO optimizes ANN weight initialization. This approach enhances the model’s ability to accurately represent seismic wave dynamics, making it well-suited for applications in vibration engineering. The dataset consisted of historical seismic records and was divided into 80% for training and 20% for testing. The ANN-PSO model outperformed conventional ANN and SVM, achieving denormalized RMSE of 0.152 (magnitude) and 0.189 (timing window likelihood error), MAE of 0.118 and 0.147, R² of 0.958 and 0.941, and Pearson r of 0.979 and 0.970 across 20 runs (MSE 0.023 on normalized scale, equivalent after denormalization). Thus, it was identified as a robust tool in the fields of vibration-based seismic forecasting, structural health monitoring, and mechanical reliability analysis in tectonically active regions.