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Title: Cyclic behavior of seismogenic sources in India and use of ANN for its prediction
Authors: Sharma, Mukat Lal
Tyagi A.
Published in: Natural Hazards
Abstract: Endeavors to realistically model physical processes responsible for earthquake occurrence and sustained large uncertainties in the results have lead to the application of techniques like artificial neural network for estimation of rate/probability of earthquake occurrence in future. The earthquake occurrence in India has been re-visited and artificial neural networks have been applied to learn the cyclic behavior of seismicity in the independent seismogenic sources to predict their future trends. As a prerequisite, the whole country has been divided into 24 seismogenic sources for which the seismicity cycles were studied. Their cyclic behavior has been captured in form of four stages of earthquake occurrence and the future trends have been predicted using ANN. To validate the trained ANN model, testing has been carried out in two ways: first, by giving the samples that are not used in training (NT) and second, by giving the total samples (T). As a method of testing, standard errors and correlation coefficients between the network output patterns and observed patterns of the testing sample given were considered. The outcome of the ANN is used to interpret the future seismicity of each of the 24 seismogenic zones in terms of various stages of the future seismicity cycles. © 2010 Springer Science+Business Media B.V.
Citation: Natural Hazards (2010), 55(2): 389-404
Issue Date: 2010
Keywords: Artificial neural networks
Seismic hazard
Seismicity cycles
ISSN: 0921030X
Author Scopus IDs: 7403269008
Author Affiliations: Sharma, M.L., Department of Earthquake Engineering, IIT Roorkee, Roorkee, India
Tyagi, A., Department of Physics, Indraprastha Engineering College, Ghaziabad, India
Corresponding Author: Sharma, M. L.; Department of Earthquake Engineering, IIT Roorkee, Roorkee, India; email:
Appears in Collections:Journal Publications [EQ]

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