Artificial neural network for tsunami forecasting

被引：20

作者：

Romano, Michele ^{[1
]}

Liong, Shie-Yui ^{[1
]}

Vu, Minh Tue ^{[1
]}

Zemskyy, Pavlo ^{[1
]}

Doan, Chi Dung ^{[1
]}

Dao, My Ha ^{[1
]}

Tkalich, Pavel ^{[1
]}

机构：

[1] Natl Univ Singapore, Trop Marine Sci Inst, Singapore 119223, Singapore

来源：

JOURNAL OF ASIAN EARTH SCIENCES | 2009年 / 36卷 / 01期

关键词：

Tsunami forecast; Data-driven model; Artificial neural network; LEVEL; PREDICTION;

D O I：

10.1016/j.jseaes.2008.11.003

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：29 / 37

页数：9

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