Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction

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...

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Main Authors:	Astuti, Winda, Akmeliawati, Rini, Sediono, Wahju, Salami, Momoh Jimoh Emiyoka
Format:	Article
Language:	English English
Published:	IEEE 2014
Subjects:	G Geography (General)
Online Access:	http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/1/06827211-publish_%281%29.pdf http://irep.iium.edu.my/37033/4/37033_Hybrid%20technique%20using%20singular%20value.SCOPUSpdf.pdf

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spelling	iium-370332017-09-18T07:27:16Z http://irep.iium.edu.my/37033/ Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction Astuti, Winda Akmeliawati, Rini Sediono, Wahju Salami, Momoh Jimoh Emiyoka G Geography (General) 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 is 2 days ahead, whereas for magnitude greater than is up to 7 days ahead. IEEE 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37033/1/06827211-publish_%281%29.pdf application/pdf en http://irep.iium.edu.my/37033/4/37033_Hybrid%20technique%20using%20singular%20value.SCOPUSpdf.pdf Astuti, Winda and Akmeliawati, Rini and Sediono, Wahju and Salami, Momoh Jimoh Emiyoka (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). pp. 1719-1728. ISSN 1939-1404 http://dx.doi.org/10.1109/JSTARS.2014.2321972 doi:10.1109/JSTARS.2014.2321972
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	G Geography (General)
spellingShingle	G Geography (General) Astuti, Winda Akmeliawati, Rini Sediono, Wahju Salami, Momoh Jimoh Emiyoka Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
description	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 is 2 days ahead, whereas for magnitude greater than is up to 7 days ahead.
format	Article
author	Astuti, Winda Akmeliawati, Rini Sediono, Wahju Salami, Momoh Jimoh Emiyoka
author_facet	Astuti, Winda Akmeliawati, Rini Sediono, Wahju Salami, Momoh Jimoh Emiyoka
author_sort	Astuti, Winda
title	Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
title_short	Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
title_full	Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
title_fullStr	Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
title_full_unstemmed	Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction
title_sort	hybrid technique using singular value decomposition (svd) and support vector machine (svm) approach for earthquake prediction
publisher	IEEE
publishDate	2014
url	http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/ http://irep.iium.edu.my/37033/1/06827211-publish_%281%29.pdf http://irep.iium.edu.my/37033/4/37033_Hybrid%20technique%20using%20singular%20value.SCOPUSpdf.pdf
first_indexed	2023-09-18T20:53:07Z
last_indexed	2023-09-18T20:53:07Z
_version_	1777410126624325632

Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction

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