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AI based Earthquake Forecasting in Portugal
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Autores
Orientador(es)
Resumo(s)
In this work, the potential of artificial intelligence for forecasting earthquakes in Portugal, a
region under looked with only the Western Azores area being previously studied, is
investigated. The target of this research is set to prediction of an event occurrence with a
magnitude higher than 4.0 in the next 10 days. Seismic sequential data retrieved from an
open-source catalogue is leveraged through machine learning models (Naïve Bayes, K-Nearest
Neighbours, Support Vector Machine, Random Forest and neural networks focusing on the
application of LSTM layers) to predict earthquake occurrences. The study involves an
extensive data pre-processing with feature extraction by exploiting geophysical formulas and
multiple strategiesto capture the sequential information. A study on the performance of each
strategy and model is conducted in an attempt to identify if a methodology or algorithm
should be preferred on earthquake studies in the area under analysis. The final results
showcase some advancements in terms of state of the art and introduce new discoveries but
still lack the minimum quality to consider the deployment of such warning systems on reallife. This thesis contributes to the development of AI-driven earthquake prediction systems in
Portugal and suggests directions for future improvements
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Earthquake Prediction LSTM Machine Learning Portugal Sequential Data SDG 3 - Good health and well-being SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities