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    Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

    186718_186718.pdf (572.9Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Jian, Le
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, Kit and Jian, Le. 2013. Identification of significant factors for air pollution levels using a neural network based knowledge discovery system. Neurocomputing. 99: 564-569.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2012.06.003
    ISSN
    09252312
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Vol. 99, doi: 10.1016/j.neucom.2012.06.003

    URI
    http://hdl.handle.net/20.500.11937/22793
    Collection
    • Curtin Research Publications
    Abstract

    Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels.

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