A parallel machine learning-based approach for tsunami waves forecasting using regression trees

Abstract

Following a seismic event, tsunami early warning systems (TEWSs) try to provide precise forecasts of the maximum height of incoming waves at designated target points along the coast. This information is crucial to trigger early warnings in areas where the impact of tsunami waves is predicted to be dangerous (or potentially cause destruction), to help the management of the potential impact of a tsunami as well as reduce environmental destruction and losses of human lives. For such a reason, it is crucial that TEWSs produce predictions with short computation time while maintaining a high prediction accuracy. This paper presents a parallel machine learning approach, based on regression trees, to discover tsunami predictive models from simulation data. In order to achieve the results in a short time, the proposed approach relies on the parallelization of the most time consuming tasks and on incremental learning executions, in order to achieve higher performances in terms of execution time, efficiency and scalability. The experimental evaluation, performed on two real tsunami cases occurred in the Western and Eastern Mediterranean basin in 2003 and 2017, shows reasonable advantages in terms of scalability and execution time, which is an important benefit in a urgent-computing scenarios.