Real-valued negative selection algorithm for abnormal earthquake detection

Earthquake prediction has been a research topic for many years. Many attempts have been made to predict the behavior of earthquake. However, there is yet another field of interest that is seldom explored by the researchers, which is detecting the abnormal behavior of the earthquake. The earthquake m...

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Main Author:	Hasan, Zeyad Abd. Algfoor
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Unspecified
Online Access:	http://eprints.utm.my/id/eprint/33403/1/ZeyadAbdAlgfoorHasanMFC2010.pdf
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id	my-utm-ep.33403
record_format	uketd_dc
spelling	my-utm-ep.334032021-05-27T06:33:24Z Real-valued negative selection algorithm for abnormal earthquake detection 2012 Hasan, Zeyad Abd. Algfoor Unspecified Earthquake prediction has been a research topic for many years. Many attempts have been made to predict the behavior of earthquake. However, there is yet another field of interest that is seldom explored by the researchers, which is detecting the abnormal behavior of the earthquake. The earthquake magnitude detection studies based on the analysis of historical earthquake data assumes a temporal model. Such models describe the frequencies of occurrence of seismic events as functions of their magnitudes. The most widely used magnitude-frequency model for hazard estimation is that based on the Gutenberg-Richter inverse power law. Artificial Immune System (AIS) has been a common approach in pattern recognition, optimization and many others. However, the application of AIS in the detection of abnormal earthquake behavior is still a new and challenging experience. In this study, Real-Valued Negative Selection Algorithm (RNSA) in AIS is used to establish a model of normal behavior from the large amount of earthquake data and to detect if elements of the data set have changed from an established norm. To show the applicability of the RNSA in abnormal earthquake detection, the earthquake data are divided into several segments and tested according to the assumed normal distribution. Simulation results have revealed that the RNSA improves the performance in terms of detection rate was 87% and 57% for false alarm rate with 8 features. 2012 Thesis http://eprints.utm.my/id/eprint/33403/ http://eprints.utm.my/id/eprint/33403/1/ZeyadAbdAlgfoorHasanMFC2010.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:79678?queryType=vitalDismax&query=Real-valued+negative+selection+algorithm+for+abnormal+earthquake+detection&public=true masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Unspecified
spellingShingle	Unspecified Hasan, Zeyad Abd. Algfoor Real-valued negative selection algorithm for abnormal earthquake detection
description	Earthquake prediction has been a research topic for many years. Many attempts have been made to predict the behavior of earthquake. However, there is yet another field of interest that is seldom explored by the researchers, which is detecting the abnormal behavior of the earthquake. The earthquake magnitude detection studies based on the analysis of historical earthquake data assumes a temporal model. Such models describe the frequencies of occurrence of seismic events as functions of their magnitudes. The most widely used magnitude-frequency model for hazard estimation is that based on the Gutenberg-Richter inverse power law. Artificial Immune System (AIS) has been a common approach in pattern recognition, optimization and many others. However, the application of AIS in the detection of abnormal earthquake behavior is still a new and challenging experience. In this study, Real-Valued Negative Selection Algorithm (RNSA) in AIS is used to establish a model of normal behavior from the large amount of earthquake data and to detect if elements of the data set have changed from an established norm. To show the applicability of the RNSA in abnormal earthquake detection, the earthquake data are divided into several segments and tested according to the assumed normal distribution. Simulation results have revealed that the RNSA improves the performance in terms of detection rate was 87% and 57% for false alarm rate with 8 features.
format	Thesis
qualification_level	Master's degree
author	Hasan, Zeyad Abd. Algfoor
author_facet	Hasan, Zeyad Abd. Algfoor
author_sort	Hasan, Zeyad Abd. Algfoor
title	Real-valued negative selection algorithm for abnormal earthquake detection
title_short	Real-valued negative selection algorithm for abnormal earthquake detection
title_full	Real-valued negative selection algorithm for abnormal earthquake detection
title_fullStr	Real-valued negative selection algorithm for abnormal earthquake detection
title_full_unstemmed	Real-valued negative selection algorithm for abnormal earthquake detection
title_sort	real-valued negative selection algorithm for abnormal earthquake detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2012
url	http://eprints.utm.my/id/eprint/33403/1/ZeyadAbdAlgfoorHasanMFC2010.pdf
_version_	1747816152784961536

Real-valued negative selection algorithm for abnormal earthquake detection

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