Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions

Operational TEWS play a key role in reducing tsunami impact on populated coastal areas around the world in the event of an earthquake-generated tsunami. Traditionally, these systems in the NEAM region have relied on the implementation of decision matrices. The very short arrival times of the tsunami waves from generation to impact in this region have made it not possible to use real-time on-the-fly simulations to produce more accurate alert levels. In these cases, when time restriction is so demanding, an alternative to the use of decision matrices is the use of datasets of precomputed tsunami scenarios. In this paper we propose the use of neural networks to predict the tsunami maximum height and arrival time in the context of TEWS. Different neural networks were trained to solve these problems. Additionally, ensemble techniques were used to obtain better results.This work was funded by “Innovative ecosystem with artificial intelligence for Andalusia 20205” project of CEI Andalucía Tech and University of Málaga, UMA-CEIATECH-05. The numerical results presented in this work were performed with the computational resources provided by the Spanish Network for Supercomputing (RES) grants AECT-2020-1-0009 and AECT-2020-2-0001. Finally, this research has been partially supported by the Spanish Government research project MEGAFLOW (RTI2018-096064-B-C21), ChEESE project (EU Horizon 2020, grant agreement N. 823844), and eFlows4HPC project (funded by the EuroHPC JU under contract 955558 and the Ministerio de Ciencia e Innovación, Spain). Partial funding for open access charge: Universidad de Málag