2026-05-27T23:19:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/243782026-02-03T11:11:03Zcom_10630_2254col_10630_37953

Rodríguez Gálvez, Juan Francisco Macías-Sánchez, Jorge Castro-Díaz, Manuel Jesús De-la-Asunción-Hernández, Marc 2022-06-15T10:22:02Z 2022-06-15T10:22:02Z 2022-06-13 Rodríguez JF, Macías J, Castro MJ, de la Asunción M, Sánchez-Linares C. Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions. GeoHazards. 2022; 3(2):323-344. https://doi.org/10.3390/geohazards3020017 https://hdl.handle.net/10630/24378 10.3390/geohazards3020017 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. eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Maremotos Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions journal article