TSUNAMI EARLY WARNING SYSTEM - AN INDIAN OCEAN PERSPECTIVE

被引:7
作者
Kumar, B. Prasad [1 ]
Kumar, R. Rajesh [1 ]
Dube, S. K. [2 ]
Rao, A. D. [2 ]
Murty, Tad [3 ]
Gangopadhyay, Avijit [4 ]
Chaudhuri, Ayan [4 ]
机构
[1] Indian Inst Technol, Dept Ocean Engn & Naval Architecture, Kharagpur 721302, W Bengal, India
[2] Indian Inst Technol, Ctr Atmospher Sci, New Delhi 110016, India
[3] Univ Ottawa, Dept Civil Engn, Ottawa, ON K1N 6N5, Canada
[4] Univ Massachusetts Dartmouth, Sch Marine Sci & Technol, Dartmouth, MA 02719 USA
关键词
Tsunami; travel time; neural networks; warning system;
D O I
10.1142/S1793431108000311
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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 degrees N, 95.7 degrees 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.
引用
收藏
页码:197 / 226
页数:30
相关论文
共 48 条
  • [1] ALEXEEV AS, 1977, REPORT SER DEP FISHE, V48, P37
  • [2] [Anonymous], P IEEE INT C NEUR NE
  • [3] [Anonymous], 1998, NEURAL NETWORKS COMP
  • [4] [Anonymous], 1990, BEHAV SCI LAW, DOI DOI 10.1002/BSL.2370080108
  • [5] BAPAT A, 1983, CATALOG EARTHQUAKES
  • [6] Tsunami travel time prediction using neural networks
    Barman, Rahul
    Kumar, B. Prasad
    Pandey, P. C.
    Dube, S. K.
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2006, 33 (16)
  • [7] BINDRA S, 2005, TSUNAMI 7 HOURS SHOO, P291
  • [8] BISHOP CM, 1995, NEURAL NETWORKS PATT, P364
  • [9] BRADDOCK RD, 1981, MAR GEOD, V5, P256
  • [10] Chapman C., 2005, EOS Trans. Am. Geophys. Union, V86, P13, DOI [10.1029/2005EO020003, DOI 10.1029/2005EO020003]