Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
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Date
2014-06-06
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Most of the existing earthquake (EQ) prediction techniques involve a combination of
signal processing and geophysics techniques which are relatively complex in computation
for analysis of the Earth's electric field data. This paper proposes a relatively simpler and
faster method that involves only signal processing procedures. The prediction of the EQ
occurrence estimation using a combination of singular value decomposition (SVD)-based
technique for feature extraction and support vector machine (SVM) classifier is presented
in this paper. Using the proposed method, the Earth's electric field signal is transformed
into a new domain using SVD-based approach. In this approach, the time domain signal
is projected on the left eigenvectors of impulse response matrix of the linear prediction
coefficient (LPC) filter. Several features have been extracted from the transformed signal.
These features are used as input for the SVM classifier in order to predict the location of
the forthcoming EQ. Once the location is determined, a similar approach is used to
estimate its magnitude. Finally, the time estimation of the forthcoming EQ is estimated
based on the statistical observation. The occurred EQs during 2008 in Greece are used
to train the classifiers, whereas those occurred from 2003 to 2010 in the same region are
used to evaluate the performance of the proposed system. In predicting the location of
the future EQs, the proposed system could achieve 77% accuracy. As for the magnitude
prediction, the proposed system provides an accuracy of 66.67%. Moreover, the
predicted time for the EQ with magnitude greater than Ms = 5 is 2 days ahead, whereas
for magnitude greater than Ms = 6 is up to 7 days ahead.
Description
Keywords
Earth, Support vector machines, Feature extraction, Monitoring, Estimation, Databases, Training
Citation
Astuti, W., Akmeliawati, R., Sediono, W., & Salami, M. J. E. (2014). Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5), 1719-1728.