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<p>Earthquake prediction is vital for mitigating seismic risks, especially in regions like the Mediterranean, prone to frequent seismic activity. This study evaluates the application of machine learning techniques: linear regression, long short-term memory, bidirectional long short-term memory, convolutional neural network, time series analysis, and the informer model, using historical seismic data to predict earthquake occurrences. The models demonstrated notable predictive capabilities, revealing trends and patterns in earthquake activity across the region. The analysis highlights the importance of high-quality, diverse datasets and robust validation methods to improve prediction accuracy. Despite challenges, such as data imbalances and model limitations, the findings provide valuable insights for refining earthquake forecasting models and enhancing regional seismic risk management strategies.</p>

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Seismic prediction in the mediterranean: AI-driven forecasting models

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Imen Ziadi,
  • Nejla Essaddi,
  • Mongi Besbes

摘要

Earthquake prediction is vital for mitigating seismic risks, especially in regions like the Mediterranean, prone to frequent seismic activity. This study evaluates the application of machine learning techniques: linear regression, long short-term memory, bidirectional long short-term memory, convolutional neural network, time series analysis, and the informer model, using historical seismic data to predict earthquake occurrences. The models demonstrated notable predictive capabilities, revealing trends and patterns in earthquake activity across the region. The analysis highlights the importance of high-quality, diverse datasets and robust validation methods to improve prediction accuracy. Despite challenges, such as data imbalances and model limitations, the findings provide valuable insights for refining earthquake forecasting models and enhancing regional seismic risk management strategies.