Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jseaes.2008.11.003
Title: Artificial neural network for tsunami forecasting
Authors: Romano, M. 
Liong, S.-Y. 
Vu, M.T. 
Zemskyy, P. 
Doan, C.D. 
Dao, M.H. 
Tkalich, P. 
Keywords: Artificial neural network
Data-driven model
Tsunami forecast
Issue Date: 4-Sep-2009
Citation: Romano, M., Liong, S.-Y., Vu, M.T., Zemskyy, P., Doan, C.D., Dao, M.H., Tkalich, P. (2009-09-04). Artificial neural network for tsunami forecasting. Journal of Asian Earth Sciences 36 (1) : 29-37. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jseaes.2008.11.003
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.
Source Title: Journal of Asian Earth Sciences
URI: http://scholarbank.nus.edu.sg/handle/10635/115004
ISSN: 13679120
DOI: 10.1016/j.jseaes.2008.11.003
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