Abstract
Within the current seismology domain, earthquake magnitude prediction has become paramount since conventional approaches often need to improve precision and prognostic capability. This study discusses the urgent need for a prediction model that is more precise and dependable. The study presents a novel approach that utilizes sophisticated artificial neural networks (ANNs) and incorporates the tabu-search technique for hyperparameter tweaking to improve the model. The research employs a rigorous methodology using a comprehensive dataset that documents occurrences of earthquakes. The artificial neural network (ANN) model is trained across 50 epochs, with a batch size of 32. The key results demonstrate a significant R-squared value of 33.9%, indicating the improved predictive capacity of the model in estimating earthquake magnitudes. The mean absolute error (MAE) highlights its precision by exhibiting a variance of just 0.0806 units. The present study signifies a groundbreaking methodology for forecasting earthquake magnitudes, which has significant ramifications for seismic engineering and safety protocols.
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The data that support the findings of this study are available from the corresponding author, upon request.
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WHA and MB wrote the main manuscript text, HHA and NAA prepared figures 1–3. All authors reviewed the manuscript.
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Al Yamani, W.H., Bisharah, M., Alumany, H.H. et al. Machine learning in seismic structural design: an exploration of ANN and tabu-search optimization. Asian J Civ Eng 25, 2367–2377 (2024). https://doi.org/10.1007/s42107-023-00913-w
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DOI: https://doi.org/10.1007/s42107-023-00913-w