TSUNAMI EARLY WARNING SYSTEM — AN INDIAN OCEAN PERSPECTIVE

Author:

KUMAR B. PRASAD1,KUMAR R. RAJESH1,DUBE S. K.2,RAO A. D.2,MURTY TAD3,GANGOPADHYAY AVIJIT4,CHAUDHURI AYAN4

Affiliation:

1. Department of Ocean Engineering & Naval Architecture, Indian Institute of Technology, Kharagpur — 721 302, West Bengal, India

2. Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi — 110 016, India

3. Department of Civil Engineering, University of Ottawa, Ottawa, Canada

4. School of Marine Science & Technology, University of Massachussets at Dartmouth, MA 02719, USA

Abstract

On 26th December 2004, the countries within the vicinity of East Indian Ocean experienced the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4°N, 95.7°E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT). One of the most basic information that any tsunami warning center should have at its disposal, is information on Tsunami Travel Times (TTT) to various coastal locations surrounding the Indian Ocean rim, as well as to several island locations. Devoid of this information, no ETA's (expected times of arrival) can be included in the real-time tsunami warnings. The work describes on development of a comprehensive TTT atlas providing ETA's to various coastal destinations in the Indian Ocean rim. This Atlas was first released on the first anniversary of the Indian Ocean Tsunami and was dedicated to the victims. Application of soft computing tools like Artificial Neural Network (ANN) for prediction of ETA can be immensely useful in a real-time mode. The major advantage of using ANN in a real-time tsunami travel time prediction is its high merit in producing ETA at a much faster time and also simultaneously preserving the consistency of prediction. Overall, it can be mentioned that modern technology can prevent or help in minimizing the loss of life and property provided we integrate all essential components in the warning system and put it to the best possible use.

Publisher

World Scientific Pub Co Pte Lt

Subject

Geophysics,Geotechnical Engineering and Engineering Geology,Oceanography

Reference41 articles.

1. Tsunami travel time prediction using neural networks

2. S. Bindra, Tsunami: 7 Hours that Shook the World (Harper Collins Publications, New Delhi, India, 2005) p. 291.

3. C. M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, UK, 1995) pp. 364–369.

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