Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks.

ABSTRACT: Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inund

SUBMITTER: Nunez J 

PROVIDER: S-EPMC9209464 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature