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JACIII Vol.18 No.5 pp. 701-713
doi: 10.20965/jaciii.2014.p0701
(2014)

Paper:

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Artificial Neural Networks for Earthquake Anomaly Detection

Aditya Sriram, Shahryar Rahanamayan, and Farid Bourennani

University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario L1H 7K4, Canada

Received:
October 29, 2013
Accepted:
March 6, 2014
Published:
September 20, 2014
Creative Commons License
Keywords:
artificial neural networks, ANN, earthquake anomalies detection, precursor, earthquake prediction
Abstract
Earthquakes are natural disasters caused by an unexpected release of seismic energy from extreme levels of stress within the earth’s crust. Over the years, earthquake prediction has been a controversial research subject that has challenged even the smartest ofminds. Because numerous seismic precursors and other factors exist that may indicate the potential of an earthquake occurring, it is extremely difficult to predict the exact time, location, and magnitude of an impending quake. Nevertheless, evaluating a combination of these precursors through advances in Artificial Intelligence (AI) can certainly increase the possibility of predicting an earthquake. The sole purpose for predicting a seismic event at a pre-determined locality is to provide substantial time for the citizens to take precautionary measures. With this in mind, Artificial Neural Networks (ANNs) have been promising techniques for the detection and prediction of locally impending earthquakes based on valid seismic information. To highlight the recent trends in earthquake abnormality detection, including various ideas and applications, in the field of Neural Networks, valid papers related to ANNs are reviewed and presented herein.
Cite this article as:
A. Sriram, S. Rahanamayan, and F. Bourennani, “Artificial Neural Networks for Earthquake Anomaly Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 701-713, 2014.
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