Artificial neural network for tsunami forecasting

Abstract

This paper presents a data-driven approach for effective and efficient forecasting of tsunami generated by underwater earthquakes. Based on pre-computed tsunami scenarios as training data sets the Artificial Neural Network (ANN) is used for the construction of data-driven forecasting models. The training data comprised spatial values of maximum tsunami heights and tsunami arrival times (snapshots), computed with process-based TUNAMI-N2-NUS model for the most probable ocean floor rupture scenarios. Validation tests demonstrated that with a given earthquake size and location, the ANN method provides accurate and near instantaneous forecasting of the maximum tsunami heights and arrival times for the entire computational domain. © 2008 Elsevier Ltd. All rights reserved.