26 December 2004 Indian Ocean Tsunami

2018 ◽  
Vol 92 (6) ◽  
pp. 653-656
Author(s):  
Harsh K Gupta
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami

Medieval forewarning of the 2004 Indian Ocean tsunami in Thailand

Nature ◽  
2008 ◽  
Vol 455 (7217) ◽  
pp. 1228-1231 ◽  
Author(s):  
Kruawun Jankaew ◽  
Brian F. Atwater ◽  
Yuki Sawai ◽  
Montri Choowong ◽  
Thasinee Charoentitirat ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami

A snapshot of the 2004 Indian Ocean tsunami: societal impacts and consequences

2006 ◽  
Vol 15 (1) ◽  
pp. 163-177 ◽  
Author(s):  
Havidan Rodriguez ◽  
Tricia Wachtendorf ◽  
James Kendra ◽  
Joseph Trainor
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Societal Impacts

scholarly journals Developing tsunami fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand

2011 ◽  
Vol 11 (1) ◽  
pp. 173-189 ◽  
Author(s):  
A. Suppasri ◽  
S. Koshimura ◽  
F. Imamura
Keyword(s):  
Indian Ocean ◽  
Building Materials ◽  
Fragility Curves ◽  
Distribution Functions ◽  
Least Square ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Inundation Depth ◽  
High Resolution Satellite Images ◽  
Tsunami Fragility

Abstract. The 2004 Indian Ocean tsunami damaged and destroyed numerous buildings and houses in Thailand. Estimation of tsunami impact to buildings from this event and evaluation of the potential risks are important but still in progress. The tsunami fragility curve is a function used to estimate the structural fragility against tsunami hazards. This study was undertaken to develop fragility curves using visual inspection of high-resolution satellite images (IKONOS) taken before and after tsunami events to classify whether the buildings were destroyed or not based on the remaining roof. Then, a tsunami inundation model is created to reconstruct the tsunami features such as inundation depth, current velocity, and hydrodynamic force of the event. It is assumed that the fragility curves are expressed as normal or lognormal distribution functions and the estimation of the median and log-standard deviation is performed using least square fitting. From the results, the developed fragility curves for different types of building materials (mixed type, reinforced concrete and wood) show consistent performance in damage probability and when compared to the existing curves for other locations.

scholarly journals 2004 Indian Ocean tsunami flow velocity measurements from survivor videos

2006 ◽  
Vol 33 (24) ◽  
Author(s):  
Hermann M. Fritz ◽  
Jose C. Borrero ◽  
Costas E. Synolakis ◽  
Jeseon Yoo
Keyword(s):  
Indian Ocean ◽  
Flow Velocity ◽  
Indian Ocean Tsunami ◽  
Velocity Measurements ◽  
2004 Indian Ocean Tsunami

scholarly journals Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Indian Ocean ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Deep Neural Network ◽  
Inverse Model ◽  
Indian Ocean Tsunami ◽  
Flow Conditions ◽  
2004 Indian Ocean Tsunami

Abstract. The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

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