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Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection

by Mona Peyk Herfeh, Asadollah Shahbahrami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 3
Year of Publication: 2014
Authors: Mona Peyk Herfeh, Asadollah Shahbahrami
10.5120/17507-8058

Mona Peyk Herfeh, Asadollah Shahbahrami . Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection. International Journal of Computer Applications. 100, 3 ( August 2014), 23-29. DOI=10.5120/17507-8058

@article{ 10.5120/17507-8058,
author = { Mona Peyk Herfeh, Asadollah Shahbahrami },
title = { Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 3 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number3/17507-8058/ },
doi = { 10.5120/17507-8058 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:01.406435+05:30
%A Mona Peyk Herfeh
%A Asadollah Shahbahrami
%T Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 3
%P 23-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries has been becoming more significant data for disaster management. In this paper, an approach for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this approach after extracting buildings position from digital map, they are located in the pre- and post-event images. After generating features, genetic algorithm applied for obtaining optimal features. For classification, adaptive boosting and neural networks are utilized and compared with each other. These machine learning algorithms divided the damage levels into three classes of high, moderate and low levels. Experimental results which have been obtained from Bam earthquake images show that total accuracy of adaptive boosting for detecting and classifying of collapsed through uncollapsed buildings is about 79 percent while total accuracy of neural networks is about 65 percent.

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Index Terms

Computer Science
Information Sciences

Keywords

Earthquake Collapese Detection Classification Adaptive Boosting.

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