Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions - Rodríguez Gálvez, Juan Francisco; Macías-Sánchez, Jorge; Castro-Díaz, Manuel Jesús; De-la-Asunción-Hernández, Marc (IOAP-MPDI, 2022)
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.
Keywords: Maremotos; Tsunami modeling; Deep learning; Neural network; Maximum height; Arrival time