Machine learning for Tsunamis

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machinelearning_260x220The objective of this project is to improve the Spanish Tsunami Early Warning System (TEWS) managed by the IGN and of which the EDANYA group is a scientific advisor using Machine Learning techniques. Currently this system uses the Tsunami-HySEA (TH) code for the direct simulation of hypothetical events that may affect Spain. For this, once a potentially tsunamigenic earthquake has been detected and the main characteristics such as magnitude, location, type of fault that generated it have been identified, we proceed to simulate the propagation of the tsunami, as well as the identification of the possible affected areas using the model TH. This system, although it has been a considerable advance with respect to the technology used up to now in TEWS, since it uses real-time simulations, does not take into account the initial uncertainty associated with the identification of the earthquake that generated the event. In order to include this uncertainty, it would be necessary to simultaneously perform a large number of simulations that take into account the initial uncertainty. In order to perform this task, it is intended to design a trained neural network with the possible events that may affect the Spanish coasts simulated with TH and to use the predictions obtained with the network to be able to measure the initial uncertainty of the possible tsunamigenic events.

Link to Andalucía Tech web page



  • Reference: UMA-CEIATECH-05
  •  Funding institution: Consejería de Economía, Conocimiento, Empresas y Universidad (Junta Andalucía)
  • Duration from: 1/02/2020 to 30/11/2021
  • Funding: 21,412.10€
  • PI: Jorge Macías Sánchez


What is the aim of this project?

The objective of this project is to improve the early warning system for tsunamis in Spain through the use of Machine Learning techniques to be able to take into account the uncertainty associated with predicting tsunamis in real time.

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