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

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 3
Year of Publication: 2014
Mona Peyk Herfeh
Asadollah Shahbahrami

Mona Peyk Herfeh and Asadollah Shahbahrami. Article: Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection. International Journal of Computer Applications 100(3):23-29, August 2014. Full text available. BibTeX

	author = {Mona Peyk Herfeh and Asadollah Shahbahrami},
	title = {Article: Evaluation of Adaptive Boosting and Neural Network in Earthquake Damage Levels Detection},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {100},
	number = {3},
	pages = {23-29},
	month = {August},
	note = {Full text available}


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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