Modeling Maximum Tsunami Heights Using Bayesian Neural Networks

Abstract

Tsunamis are distinguished from ordinary waves and currents owing to their characteristic longer wavelengths. Although the occurrence frequency of tsunamis is low, it can contribute to the loss of a large number of human lives as well as property damage. To date, tsunami research has concentrated on developing numerical models to predict tsunami heights and run-up heights with improved accuracy because hydraulic experiments are associated with high costs for laboratory installation and maintenance. Recently, artificial intelligence has been developed and has revealed outstanding performance in science and engineering fields. In this study, we estimated the maximum tsunami heights for virtual tsunamis. Tsunami numerical simulation was performed to obtain tsunami height profiles for historical tsunamis and virtual tsunamis. Subsequently, Bayesian neural networks were employed to predict maximum tsunami heights for virtual tsunamis.