Tsunami Early Warning System — An Indian Ocean Perspective

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.

Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology

“…The advantages of artificial intelligence technology for tsunami prediction have been widely stated, especially related to the tsunamis in Aceh in 2004 and Tohuku in 2011. The mean square error of applying Artificial Neural Networks (ANN) for predicting the arrival time of tsunamis in the Indian Ocean was 0.25 [16], [17]. The ANN forecasting model only takes a few seconds to provide data with accuracy similar to a typical tsunami propagation model, such as TUNAMI-N2, which requires 10 minutes of Central Processing Unit (CPU) time on a standard desktop PC [18], [19].…”

Earthquake Generated Tsunami in the Indian Ocean and Probable Vulnerability Assessment for the East Coast of India

“…It is clear from above descriptions that tsunami waves threaten all communities along the coast, and it is imperative to assess the impact as well as the various measures that can be taken to safeguard against the tsunami disaster. Even when the monitoring systems are in place, it is difficult to accurately predict the timing, impact and the extent of inundation along the coastal areas (Prasad Kumar et al , 2008Jaiswal et al 2009). Inundation and travel time of tsunami wave maps for the region are an imperative input for the evacuation process to be carried out in case of a disaster, but the lack of data is a hindrance in effective relief work as well as management of the evacuation of the people.…”