Seismic prediction in the mediterranean: AI-driven forecasting models

Imen Ziadi (), Nejla Essaddi () and Mongi Besbes ()
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Imen Ziadi: University of Tunis El Manar
Nejla Essaddi: University of Carthage
Mongi Besbes: University of Tunis El Manar

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 10, No 33, 12123-12167

Abstract: Abstract 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.

Keywords: Earthquake prediction; Seismic activity; Mediterranean regions; Linear regression; Time series; LSTM; BiLSTM; CNN; Informer (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07275-3

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