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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/5800

    Title: Statistical Diagnosis of Groundwater Quality Data in Kaohsiung Science Park
    Authors: Ting-Nien Wu(吳庭年)
    Chen-Hsiang Huang
    Keywords: principal component analysis
    data mining
    groundwater contamination
    water quality
    cluster analysis
    Date: 2009
    Issue Date: 2009-11-18 09:37:29 (UTC+8)
    Abstract: This study integrated conventional statistical tools
    with neighbourhood linkage to propose the statistical
    diagnosis approach. Fourteen monitoring wells in
    Kaohsiung Science Park were selected as study case,
    and lab data of routine groundwater analysis including
    pH, EC, hardness, TDS, TOC, ammonia, nitrate, nitrite,
    chloride, sulphate, fluoride, phenols, Fe, Mn, As, and
    temperature were subjected to principal component and
    cluster analysis. Principal component analysis (PCA)
    was utilized to reflect those chemical data with the
    greatest correlation, and PCA results identified five
    major principal components (PCs) representing 74.6%
    of cumulative variance. Based on the monitoring data
    between 2005 and 2008, the extracted information from
    the PCA mirrored the potential sources of groundwater
    contamination as acid leakage, arsenic dissolution,
    salinization, mineralization, and fluoride release.
    Cluster analysis (CA) was used to evaluate the
    similarities of water quality in groundwater samples,
    and five clusters were assigned in two-step clustering
    for corresponding with the number of PCs, i.e. the
    potential sources of groundwater contamination. The
    interpreted facts from CA illustrated that the classified
    monitoring wells in each cluster properly match up with
    the identified processes. With the aid of neighbourhood
    linkage, the domain of groundwater contamination can
    be spatially outlined by mapping the neighbouring wells
    within the identical cluster. Therefore, the nature of
    underlying processes affecting groundwater quality was
    explored by statistical diagnosis.
    Appears in Collections:[環境工程系所] 期刊論文

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