Designing a Model for Earthquake Timing and Magnitude Prediction based on Neural Networks and Particle Swarm Optimization (PSO) Algorithm

Designing a Model for Earthquake Timing and Magnitude Prediction based on Neural Networks and Particle Swarm Optimization (PSO) Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Designing a Model for Earthquake Timing and Magnitude Prediction based on Neural Networks and Particle Swarm Optimization (PSO) Algorithm Ramin Vafaei Poursorkhabi, Ata Rezaei Fard, Ali Rezaei Fard This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7979190/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract The present study offers a hybrid predictive model integrating Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) as a way of forecasting earthquake timing and magnitude in Saman, Iran, having 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, and this can enhance the ability of the model to capture seismic wave dynamics for applications relevant to vibration engineering. The dataset, made up of historical seismic records, was split into 80% for training and 20% for testing. The ANN-PSO model showed better performance compared to the conventional ANN and Support Vector Machine (SVM) methods and achieved an average accuracy of 94.1% for magnitude and 91.7% for timing, with a mean squared error (MSE) determined at 0.023. Precision and recall rates were determined at 92.8% and 93.4%, respectively, and the training time decreased by 26% compared to standard ANN implementations. The model, validated over 20 independent runs and using dynamic measurement experiments, showed consistent performance. Thus, it was identified as a robust tool in the field of vibration-based seismic forecasting, structural health monitoring, and mechanical reliability analysis in regions that are tectonically active. Earthquake prediction Artificial Neural Networks (ANN) Particle Swarm Optimization (PSO) Vibration signal analysis Peak ground acceleration (PGA) Seismic wave dynamics Structural health monitoring Tectonically active regions Full Text Additional Declarations No competing interests reported. Tables are available in the Supplementary Files section. Supplementary Files TablesR0.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviews received at journal 24 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor invited by journal 30 Oct, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 29 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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