Fire data analysis and feature reduction using computational intelligence methods

M. Bahrepour; B.J. van der Zwaag; Nirvana Meratnia; Paul J.M. Havinga

doi:10.1007/978-3-642-14616-9_28

Fire data analysis and feature reduction using computational intelligence methods

M. Bahrepour, B.J. van der Zwaag, Nirvana Meratnia, Paul J.M. Havinga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

12 Citations (Scopus)

678 Downloads (Pure)

Abstract

Fire is basically the fast oxidation of a substance that produces gases and chemical productions. These chemical productions can be read by sensors to yield an insight about type and place of the fire. However, as fires may occur in indoor or outdoor areas, the type of gases and therefore sensor readings become different. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. In this paper, the authors investigate spatial analysis of data for indoor and outdoor fires using data-mining approaches for WSN-based fire detection purposes. This paper also delves into correlated data features in fire data sets and investigates the most contributing features for fire detection applications.

Original language	Undefined
Title of host publication	Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010
Editors	G. Phillips-Wren, L.C. Jain, K. Nakamatsu
Place of Publication	Berlin Heidelberg
Publisher	Springer
Pages	289-298
Number of pages	10
ISBN (Print)	978-3-642-14615-2
DOIs	https://doi.org/10.1007/978-3-642-14616-9_28
Publication status	Published - 23 Jul 2010
Event	2nd KES International Symposium on Intelligent Decision Technologies 2010 - Inner Harbor, Baltimore, Baltimore, United States Duration: 28 Jul 2010 → 30 Jul 2010 Conference number: 2

Publication series

Name	Smart Innovation, Systems and Technologies
Publisher	Springer-Verlag
Volume	4
ISSN (Print)	2190-3018

Conference

Conference	2nd KES International Symposium on Intelligent Decision Technologies 2010
Abbreviated title	KES IDT 2010
Country	United States
City	Baltimore
Period	28/07/10 → 30/07/10

Keywords

IR-72520
EWI-18253
EC Grant Agreement nr.: FP7/215923
METIS-270961

Access to Document

10.1007/978-3-642-14616-9_28

ISDA2010paper
open
ISDA2010paper
open

http://eprints.eemcs.utwente.nl/secure2/18253/02/00040289.pdf

Cite this

Bahrepour, M., van der Zwaag, B. J., Meratnia, N., & Havinga, P. J. M. (2010). Fire data analysis and feature reduction using computational intelligence methods. In G. Phillips-Wren, L. C. Jain, & K. Nakamatsu (Eds.), Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010 (pp. 289-298). (Smart Innovation, Systems and Technologies; Vol. 4). Springer. https://doi.org/10.1007/978-3-642-14616-9_28

Bahrepour, M. ; van der Zwaag, B.J. ; Meratnia, Nirvana ; Havinga, Paul J.M. / Fire data analysis and feature reduction using computational intelligence methods. Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010. editor / G. Phillips-Wren ; L.C. Jain ; K. Nakamatsu. Berlin Heidelberg : Springer, 2010. pp. 289-298 (Smart Innovation, Systems and Technologies).

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abstract = "Fire is basically the fast oxidation of a substance that produces gases and chemical productions. These chemical productions can be read by sensors to yield an insight about type and place of the fire. However, as fires may occur in indoor or outdoor areas, the type of gases and therefore sensor readings become different. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. In this paper, the authors investigate spatial analysis of data for indoor and outdoor fires using data-mining approaches for WSN-based fire detection purposes. This paper also delves into correlated data features in fire data sets and investigates the most contributing features for fire detection applications.",

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Bahrepour, M, van der Zwaag, BJ , Meratnia, N & Havinga, PJM 2010, Fire data analysis and feature reduction using computational intelligence methods. in G Phillips-Wren, LC Jain & K Nakamatsu (eds), Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010. Smart Innovation, Systems and Technologies, vol. 4, Springer, Berlin Heidelberg, pp. 289-298, 2nd KES International Symposium on Intelligent Decision Technologies 2010, Baltimore, United States, 28/07/10. https://doi.org/10.1007/978-3-642-14616-9_28

Fire data analysis and feature reduction using computational intelligence methods. / Bahrepour, M.; van der Zwaag, B.J.; Meratnia, Nirvana ; Havinga, Paul J.M.

Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010. ed. / G. Phillips-Wren; L.C. Jain; K. Nakamatsu. Berlin Heidelberg : Springer, 2010. p. 289-298 (Smart Innovation, Systems and Technologies; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - Fire is basically the fast oxidation of a substance that produces gases and chemical productions. These chemical productions can be read by sensors to yield an insight about type and place of the fire. However, as fires may occur in indoor or outdoor areas, the type of gases and therefore sensor readings become different. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. In this paper, the authors investigate spatial analysis of data for indoor and outdoor fires using data-mining approaches for WSN-based fire detection purposes. This paper also delves into correlated data features in fire data sets and investigates the most contributing features for fire detection applications.

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Bahrepour M, van der Zwaag BJ , Meratnia N , Havinga PJM. Fire data analysis and feature reduction using computational intelligence methods. In Phillips-Wren G, Jain LC, Nakamatsu K, editors, Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010. Berlin Heidelberg: Springer. 2010. p. 289-298. (Smart Innovation, Systems and Technologies). https://doi.org/10.1007/978-3-642-14616-9_28