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


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

October 29, 2013
March 6, 2014
September 20, 2014
artificial neural networks, ANN, earthquake anomalies detection, precursor, earthquake prediction
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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