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 proceedingConference contributionAcademicpeer-review

    14 Citations (Scopus)
    1164 Downloads (Pure)


    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 languageUndefined
    Title of host publicationAdvances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010
    EditorsG. Phillips-Wren, L.C. Jain, K. Nakamatsu
    Place of PublicationBerlin Heidelberg
    Number of pages10
    ISBN (Print)978-3-642-14615-2
    Publication statusPublished - 23 Jul 2010
    Event2nd KES International Symposium on Intelligent Decision Technologies 2010 - Inner Harbor, Baltimore, Baltimore, United States
    Duration: 28 Jul 201030 Jul 2010
    Conference number: 2

    Publication series

    NameSmart Innovation, Systems and Technologies
    ISSN (Print)2190-3018


    Conference2nd KES International Symposium on Intelligent Decision Technologies 2010
    Abbreviated titleKES IDT 2010
    Country/TerritoryUnited States


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

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